CN112612942A - Social big data-based fund recommendation system and method - Google Patents
Social big data-based fund recommendation system and method Download PDFInfo
- Publication number
- CN112612942A CN112612942A CN202011596610.4A CN202011596610A CN112612942A CN 112612942 A CN112612942 A CN 112612942A CN 202011596610 A CN202011596610 A CN 202011596610A CN 112612942 A CN112612942 A CN 112612942A
- Authority
- CN
- China
- Prior art keywords
- fund
- social
- user
- investment
- big data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 16
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 27
- 230000009193 crawling Effects 0.000 claims abstract description 12
- 238000012502 risk assessment Methods 0.000 claims abstract description 12
- 238000011156 evaluation Methods 0.000 claims abstract description 8
- 238000012216 screening Methods 0.000 claims description 11
- 230000007704 transition Effects 0.000 claims description 7
- 238000012545 processing Methods 0.000 claims description 5
- 230000003203 everyday effect Effects 0.000 claims description 4
- 230000008451 emotion Effects 0.000 claims description 3
- 230000006399 behavior Effects 0.000 abstract description 28
- 239000000047 product Substances 0.000 description 16
- 230000003044 adaptive effect Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000012546 transfer Methods 0.000 description 4
- 238000013500 data storage Methods 0.000 description 2
- 230000009191 jumping Effects 0.000 description 2
- 230000007774 longterm Effects 0.000 description 2
- 238000010009 beating Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000002457 bidirectional effect Effects 0.000 description 1
- 239000006227 byproduct Substances 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000002996 emotional effect Effects 0.000 description 1
- 238000012804 iterative process Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000012552 review Methods 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/951—Indexing; Web crawling techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9536—Search customisation based on social or collaborative filtering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0631—Item recommendations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/06—Asset management; Financial planning or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/01—Social networking
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Databases & Information Systems (AREA)
- General Physics & Mathematics (AREA)
- Finance (AREA)
- Accounting & Taxation (AREA)
- General Business, Economics & Management (AREA)
- Strategic Management (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Marketing (AREA)
- Human Resources & Organizations (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Health & Medical Sciences (AREA)
- Tourism & Hospitality (AREA)
- Computing Systems (AREA)
- Entrepreneurship & Innovation (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Technology Law (AREA)
- Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)
Abstract
The invention discloses a fund recommendation system and method based on social big data. The financial social big data acquisition module is used for crawling behavior and public opinion data of users of the financial social network site, the fund popularity analysis module is used for conducting popularity analysis on popular fund products of the financial social network site, and the fund net worth risk analysis module judges fund investment risk according to the current market average market profitability value. The recommendation system can better serve investor groups with incomplete investment and financing bases and experiences, and the recommendation method analyzes user behaviors and fund popularity on a financial social network site by designing a self-adaptive user influence algorithm, and recommends fund products suitable for investment for clients and social circle friends by combining relevant factors such as fund evaluation values and market risks.
Description
Technical Field
The invention relates to a fund recommendation system and method, in particular to a fund recommendation system and method based on social big data.
Background
Along with the gradual popularization of the public internet financing concept, the financing scale is enlarged, the characteristics of low cost, low threshold, mass customized financing scheme and the like of intelligent investment meet the financing requirements of the middle-yield level and the mass abundance level, the threshold of investment advisor service is reduced, and common financing users can enjoy the investment advisor service. However, most investors lack systematic and scientific investment concepts, the investment behavior is driven by products more, and many people have not yet contacted a recommendation system about fund investment.
The effective analysis of financial investment products and the recommendation of proper investment products to users are always the targets expected by experts and practitioners in the field of financial investment. In the age of rapid development of the internet at present, any information and public events are likely to be amplified, and in various financial social websites, public opinion information about investment products such as stocks, funds and the like is dazzling for common investors, and rational and objective choices are difficult to make. At present, the mainstream intelligent investment model and algorithm are homogenized, the risk preference evaluation of the user is very simple, and the financing requirement of an investor is presumed only through simpler questions and answers, so that the investment product which is possibly recommended to the investor and does not really meet the fund investment product of the user is possibly recommended.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a fund recommendation system based on social big data, which realizes judgment and guidance of fund investment, avoids risks to the greatest extent and improves client investment income.
The technical scheme is as follows: the fund recommendation system based on the social big data comprises a financial social big data acquisition module, a fund heat degree analysis module, a fund net worth risk analysis module and a fund screening recommendation module;
the financial social big data acquisition module is used for acquiring and processing investment preference information of the user and crawling behavior and public opinion data of the user of the financial social website;
the fund popularity analysis module is used for constructing a dynamic social topological network structure, designing a self-adaptive user influence strength algorithm and carrying out popularity analysis on popular fund products in the financial social network site;
the fund net value risk analysis module is used for judging the risk of fund investment according to the comparison of the average market profit value of the current market and historical high-level and low-level numerical values;
and the fund screening and recommending module is used for integrating results of fund evaluation, market risk analysis and fund popularity analysis, and screening and recommending fund products meeting the requirements of customers and friends in social circles of the customers.
In conclusion, the financial social big data acquisition module provides data storage for the recommended net fund value analysis and recommendation according to the emotional tendency of the user of the social network; the fund popularity analysis module introduces the self-adaptive weight factor, effectively realizes the analysis of the investment preference, the behavior and the public opinion of the user, and recommends the fund suitable for the investment preference of the client through popularity analysis on the popular fund of the financial social network site, thereby realizing the income maximization of the investor.
The invention discloses a fund recommendation method based on social big data, which comprises the following steps:
(1) collecting and processing investment preference information of a user and crawling behavior and public opinion data of a user of a financial social network site;
(2) constructing a dynamic social topological network structure, designing a self-adaptive user influence strength algorithm, and carrying out heat analysis on popular fund products in the financial social network site;
(3) judging the risk of fund investment according to the comparison of the average market profitability value of the current market with historical high-level and low-level numerical values;
(4) and (4) integrating the results of the fund valuation, the market risk analysis and the fund popularity analysis, and screening and recommending fund products meeting the requirements of the clients and friends in the social circle of the clients.
Further, in the step (1), a Hadoop-based big data platform is built to store the investment preference information of the user and the behavior and public opinion data of the user, and a MapReduce frame is adopted to clean and preprocess the financial social big data.
In the step (2), a dynamic social topological network structure is constructed, self-adaptive weight factors are introduced, a self-adaptive user influence strength algorithm is designed, and the transition probability from a user node of a social network site to a neighbor node of the user node is automatically distributed, so that the inter-node transition has certain self-adaptive characteristics, and the popularity of popular fund products on the social network site is analyzed.
Preferably, the adaptive weight factors include inter-node influence, user behavior influence and fund heat topic influence.
In the step (2), the popularity analysis is combined with the emotion index of the netizen of the financial social network site; the self-adaptive user influence degree algorithm selects the forwarding number, the comment number, the praise number, the fan number and whether the forwarding number, the comment number, the praise number and the fan number are opinion leader parameters, so that more optimal distribution of the transfer probability from the social user node to the neighbor node is realized, the node propagation capacity and the accuracy of the user influence degree measurement are effectively improved, and the heat degree of the fund can be analyzed more accurately.
And (3) acquiring real-time net value estimation, unit net value and accumulated net value of the fund product, performing incremental crawling operation every day, and judging the risk of fund investment according to the comparison of the average market profitability value of the current market and historical high-level and low-level numerical values.
In step (3), a script framework based on python collects real-time net value estimation, unit net value and accumulated net value of the fund.
In the step (3), the judgment standard of the risk of fund investment is as follows: when the average market profitability value of the market is below 10 times, the market estimation value belongs to the underestimation category; the market average market profitability value is within the range of 10-15 times, and the market average market profitability value belongs to the reasonable evaluation category; when the average market profitability value of the market is more than 15 times, the long-term investment value of the current market is reduced; and when the market average market profitability value reaches more than 20 times, the market average market profitability value belongs to the category of overestimation, and the relative risk is judged according to the interval where the market profitability value is located.
In the step (4), recommending the investment scheme meeting the requirements of the client to the client, and if the investment scheme does not meet the requirements of the client, repeating the steps (2) and (3) to adjust the combination; the fund product recommended for the client also supports the purchase of the fund directly on the existing scheme after the friends in the social circle enter the platform in a link sharing mode.
In the step (4), a recommendation scheme after user investment preference analysis is performed according to the questionnaire can be directly provided, and meanwhile, a link shared by friends in a social circle is provided to enter a platform, and fund investment is performed by directly referring to the scheme of the friends. As most users in the same social circle have similar economic abilities, the fund investment scheme of friends has higher recommendation reference value, and the function can facilitate fund investment selection of users without or with weak financing bases.
Has the advantages that: compared with the prior art, the invention has the following remarkable advantages: by analyzing the social big data, optimizing a user influence strength algorithm, constructing a dynamic social topological network structure, obtaining popular fund products of a financial social circle, comprehensively judging the risk of fund investment according to the comparison of the average market profit value of the current market and historical high-order and low-order numerical values, and finally forming an effective fund recommendation scheme.
Drawings
FIG. 1 is a system block diagram of a social big data based fund recommendation system;
FIG. 2 is a flow diagram of the operation of a social big data based fund recommendation system;
FIG. 3 is a diagram of a social network topology incorporating context nodes;
FIG. 4 is a schematic diagram of a user influence strength algorithm based on an adaptive factor.
Detailed Description
The technical solution of the present invention is further illustrated by the following examples.
The core of the invention is a fund recommendation system based on social big data, which is convenient for serving investors with weak investment and financing bases and provides a better scheme for the investors in fund investment selection. As shown in FIG. 1, the fund recommendation system based on social big data comprises a financial social big data acquisition module, a fund heat analysis module, a fund net worth risk analysis module and a fund screening recommendation module.
The financial social big data acquisition module is used for acquiring investment preference information of users and behavior and public opinion data of users crawling financial social network sites, building a big data platform storage data based on Hadoop, and cleaning and preprocessing the data by adopting a MapReduce frame;
the fund popularity analysis module constructs a dynamic social network topological structure, introduces self-adaptive weight factors (influence among nodes, user behavior influence and fund popularity topic influence), designs a self-adaptive user influence strength algorithm and carries out popularity analysis on popular funds on the financial social network site;
the fund net value risk analysis module is used for acquiring real-time net value estimation, unit net value and accumulated net value of the fund, performing incremental crawling operation every day, and judging the risk of fund investment according to the comparison of the average market profit value of the current market with historical high-level and low-level numerical values;
and the fund screening and recommending module is used for recommending appropriate fund investment products for the customers according to comprehensive screening results of fund equity risk analysis and fund popularity analysis, and can further recommend own fund purchasing schemes to be shared with friends in the social circle.
FIG. 2 is a flow chart of an embodiment of the present invention, which includes the following steps:
step 1: relevant information of the fund, basic information (including user investment preference information) in a questionnaire of the client A and user behavior data on the financial social network site are collected.
The relevant information of the fund includes not only real-time net value estimation, unit net value and accumulated net value of the fund, but also information of the fund, such as risk degree, fund size and the like, which is beneficial to the user. After the data are added to the big data storage system, the data can be updated according to the actual market conditions and the changes of the financial social network sites. The behavior data of the user comes from a snow net, an east wealth net and the like, and comprises a series of behavior messages of user posting, praise, forwarding and the like. Useful emotion evaluation information is extracted and stored in an HDFS file system at the bottom layer of Hadoop.
The big data system can adopt Hadoop, and the Hadoop is composed of components such as HDFS, MapReduce, HBase, Hive and ZooKeeper. Data collection is mainly based on the crawling framework crawling of python, and data updating crawling operation is carried out every day so as to guarantee public opinion data based on the current changed fund market and the financial social network.
Step 2: and designing an adaptive influence strength algorithm by utilizing factors such as influence among nodes, influence of user behaviors, influence of fund popularity topics and the like, analyzing the user behaviors and public sentiment data of the social network site, acquiring the popularity of fund products, and providing the popular fund investment products of the current social network site.
In a specific implementation, the step may utilize a MapReduce distributed computing framework. The parallel processing model has the characteristics of high expandability, convenience in application and the like, and the parallelization of the traditional MR _ LSH algorithm is improved on the basis of MapReduce, so that the efficient clustering of large-scale graph data can be realized in a distributed cluster environment.
And step 3: and comprehensively judging whether the fund investment is suitable for being carried out at present according to risk factors such as fund evaluation values, market profitability values and the like, and adjusting a recommendation scheme according to a judgment result. Wherein, the market risk judgment standard of fund investment is as follows: when the average market profitability value of the market is below 10 times, the market estimation value belongs to the underestimation category; the market average market profitability value is within the range of 10-15 times, and the market average market profitability value belongs to the reasonable evaluation category; when the average market profitability value of the market is more than 15 times, the long-term investment value of the current market is reduced; and when the market average market profitability value reaches more than 20 times, the market average market profitability value belongs to the category of overestimation, and the relative risk is judged according to the interval where the market profitability value is located.
During specific implementation, secondary analysis is performed on the recommendation result under the basic conditions of combining the economic capability of the client, the risk preference and the like, and when client data are analyzed, the system effectively classifies client groups by adopting a connected graph clustering algorithm based on GraphX, so that the accuracy of the recommendation result is improved.
And 4, step 4: and recommending the investment scheme of the fund to the user A, wherein the user can share the investment scheme of the fund to the user B through a social circle.
In specific implementation, the step can be mainly based on mobile-side wechat push or an applet, and further recommend and share the fund purchase scheme of the customer to the social circle friends, for example, a forwarding function is implemented by adopting an onshareepromessage (option) function in Page in the wechat applet.
In step 2, as shown in fig. 3, a background Node "is added to the social user network topology structure, and the" group Node "and all nodes in the graph are interconnected in a bidirectional manner, so that the original network G (N, M) with N nodes and M edges is converted into a strongly connected network with N +1 nodes and M +2N edges. The newly introduced nodes enable the graph transformation to be completely communicated, and meanwhile, the problem that the nodes influence the convergence and the jump parameters of the strength algorithm is solved.
After introducing the group Node, the iterative formula of the Node influence is shown as the following graph:
in the above formula, i, j is 1,2, …, N + 1. If node i is directly connected to node j, then aij1, otherwise 0,
represents the out degree of node j，
Representing the probability that node i will randomly walk to node j. All nodes except the background node have LR as initial valueiThe background node g is initialized to LR at 1g0. The LR value at node i in steady state is:
in the above formula, tcIndicating the number of convergence, LRg(tc) Is the LR value of node g in steady state. After iteration is finished, each Node obtains a stable influence value, and because the influence ranking is not included in the 'group Node', the converged influence value is averagely distributed to other nodes.
From this, an Adaptive User Influence metric Algorithm (AUIR) is further proposed, as shown in fig. 4:
(1) the probability of each node jumping to the neighbor node is equal, and each node cannot self-adaptively set the jumping probability according to the attribute behavior of the actual neighbor node. Aiming at the problem, the AUIR algorithm introduces a self-adaptive weight factor, and automatically distributes the transition probability from the user node to the neighbor nodes thereof, so that the inter-node transition has certain self-adaptive characteristics.
(2) The AUIR algorithm calculates the weight occupied by each Node based on the self-adaptive weight factor, and differentially distributes the value of 'group Node', thereby measuring the user influence value more accurately.
In the process of providing the self-adaptive weight factors, the algorithm considers three factors, namely the influence among the nodes, the influence of user behavior and the influence of fund heat topic by analyzing the node attribute behaviors.
(1) Influence between nodes
In a directed social network, the out-degree of a node represents the interest of the node, and the in-degree represents the approval of other nodes to the node. If the number of the nodes pointed by the two nodes is more, the interests of the two nodes are more similar; if the number of nodes which point to two nodes together is larger, the acceptance of the two nodes is higher, and the interests of the two nodes are similar. Therefore, if the number of common neighbor nodes between two nodes is more, that is, the number of nodes pointed to by the two nodes is more, or the number of nodes pointed to by the two nodes is more, the more similar the two nodes are, the higher the transition probability between the nodes is.
In an undirected graph, the similarity of two nodes is measured by a Jaccard similarity coefficient, and if the Jaccard value is larger, the similarity is larger, and the formula is as follows:
considering that in the directed graph, the node similarity is respectively related to the number of nodes pointed to by the node similarity and the number of nodes pointed to by the node similarity, the Jaccard metric formula is improved as shown in the following formula:
in the above formula, wn(i, j) represents the influence between nodes, the value of which is between 0 and 1, i and j are nodes in the graph respectively, n (i)inAnd n (j)inThe number of nodes pointing to i and j, respectively, n (i)outAnd n (j)outI and j respectively refer to the number of nodes pointed by i and j together.
(2) Influence of user behavior
In a social network, user behavior attributes influencing the influence of a user are more, and five representative attributes are selected in the section and are respectively: number of forwards, number of comments, number of praise, number of fan, and whether or not to comment leader. The attribute list and the identifier thereof are specifically listed in table 1:
TABLE 1 user behavior Attribute List
Here, the user behavior attributes listed in the above table are quantized, and a user behavior influence strength formula shown as follows is provided, specifically, the formula is shown as formula (5):
in the above formula, AiThe user i is the beating frequency in the statistical period, namely the activity of the user i in the statistical period. Wherein M isiThe number of beats of the user i in the T period is shown, and T is the statistical period. T isiRepresents the propagation capability of user i in a specified period, where mjIndicating any blog sent by user i during period T,
indicating Bowen mjThe forward-to-forward rate of (c),
are respectively mjThe review rate and the like. T isiThe average number of praise, the average number of comments and the average number of forwarding of the user i in the specified time period T are included. The higher the user liveness, the stronger the post propagation capacity, and the greater the impact of the user. M is the fan value of the user with the most fans in the users paying attention to i, and V (i) represents whether the i user is a large V user, if so, the I user is 1, otherwise, the I user is 0. a. b and c are respective weights.
(3) Influence of heat topic of fund
The users of the social network sites have topic bias, that is, if the similarity between the blogged text sent by the users and the topic related to the fund popularity is higher, the influence of the users on the topic is higher. Therefore, the system acquires the hot topic of a fund, the cosine similarity between news under the topic T and the blog sent by the user, and takes the value as the topic influence w of the usert(i) In that respect If wt(i) The larger the value, the more relevant the user is to the topic. Generally speaking, the wordsUsers with higher topic relevance are more concerned and are more likely to be transferred to the node by other nodes.
In order to solve the problem that each node in the influence strength algorithm cannot set the transition probability in a self-adaptive mode, the invention introduces the calculated influence among the nodes, the user behavior influence and the topic influence, and provides a self-adaptive user influence strength algorithm, namely an AUIR algorithm. The algorithm weights the node transfer probability based on the self-adaptive weight factor, so that certain behaviors, nodes and topic biases can be achieved according to different attributes of specific neighbor nodes during transfer among the nodes. The core formula of the AUIR algorithm is as follows:
equation (6) is the adaptive weight factor calculation equation, where j is the neighbor node of i, wu(j)、wt(j) User behavior influence and topic influence, w, of user j, respectivelynAnd (i, j) is the influence between the nodes of the user i and the user j, and m, n and k are the user behavior influence, the topic influence and the weight of the influence between the nodes respectively.
In the formula (7), if the node i is directly connected to the node j, aijOtherwise, it is 0. The formula is an iterative formula of an AUIR algorithm, and in the user iterative process, a self-adaptive weight factor w (i, j) is added, so that the node transfer process has certain node, behavior and topic bias.
Equation (8) is the AUIR value of Node i in a stable state, after iteration is finished, each Node obtains a stable influence value, and because the influence ranking is not counted in the 'group Node', the traditional method averagely distributes the converged influence value to other nodes. However, in view of the difference of each Node, the invention improves the formula (2), and the influence value after the convergence of the 'group Node' is distributed to each Node according to the weight proportion occupied by each Node.
Claims (7)
1. A fund recommendation system based on social big data is characterized in that: the system comprises a financial social big data acquisition module, a fund heat degree analysis module, a fund net worth risk analysis module and a fund screening recommendation module;
the financial social big data acquisition module is used for acquiring and processing investment preference information of the user and crawling behavior and public opinion data of the user of the financial social website;
the fund popularity analysis module is used for constructing a dynamic social topological network structure, designing a self-adaptive user influence strength algorithm and carrying out popularity analysis on popular fund products in the financial social network site;
the fund net worth risk analysis module is used for judging the risk size of fund investment according to the comparison of the average market profit value of the current market and historical high-level and low-level numerical values;
and the fund screening and recommending module is used for integrating results of fund evaluation, market risk analysis and fund popularity analysis, and screening and recommending fund products meeting the requirements of customers and friends in social circles of the customers.
2. A fund recommendation method based on social big data is characterized by comprising the following steps:
(1) collecting and processing investment preference information of a user and crawling behavior and public opinion data of a user of a financial social network site;
(2) constructing a dynamic social topological network structure, designing a self-adaptive user influence strength algorithm, and carrying out heat analysis on popular fund products in the financial social network site;
(3) judging the risk of fund investment according to the comparison of the average market profitability value of the current market with historical high-level and low-level numerical values;
(4) and (4) integrating the results of the fund valuation, the market risk analysis and the fund popularity analysis, and screening and recommending fund products meeting the requirements of the clients and friends in the social circle of the clients.
3. The social big data-based fund recommendation method according to claim 2, wherein: in the step (2), a dynamic social topological network structure is constructed, self-adaptive weight factors are introduced, a self-adaptive user influence strength algorithm is designed, and the transition probability from a user node of a social network site to a neighbor node of the user node is automatically distributed.
4. The social big data-based fund recommendation method according to claim 3, wherein: the self-adaptive weight factors comprise influence among nodes, user behavior influence and fund heat topic influence.
5. The social big data-based fund recommendation method according to claim 2, wherein: and (3) acquiring real-time net value estimation, unit net value and accumulated net value of the fund product, performing incremental crawling operation every day, and judging the risk of fund investment according to the comparison of the average market profitability value of the current market and historical high-level and low-level numerical values.
6. The social big data-based fund recommendation method according to claim 2, wherein: in the step (2), the popularity analysis is combined with the emotion index of the netizen of the financial social network site; the self-adaptive user influence strength algorithm selects the forwarding number, the comment number, the praise number, the vermicelli number and whether the parameters are opinion leader parameters.
7. The social big data-based fund recommendation method according to claim 2, wherein: in the step (4), recommending the investment scheme meeting the requirements of the client to the client, and if the investment scheme does not meet the requirements of the client, repeating the steps (2) and (3) to adjust the combination; the fund product recommended for the customer also supports the purchase of the fund directly on the existing scheme after the friends in the social circle enter the platform in a link sharing mode.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011596610.4A CN112612942B (en) | 2020-12-29 | 2020-12-29 | Social big data-based fund recommendation system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011596610.4A CN112612942B (en) | 2020-12-29 | 2020-12-29 | Social big data-based fund recommendation system and method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112612942A true CN112612942A (en) | 2021-04-06 |
| CN112612942B CN112612942B (en) | 2022-09-23 |
Family
ID=75248858
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011596610.4A Active CN112612942B (en) | 2020-12-29 | 2020-12-29 | Social big data-based fund recommendation system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112612942B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112966189A (en) * | 2021-04-14 | 2021-06-15 | 刘蒙 | Fund product recommendation system |
| CN113240472A (en) * | 2021-05-19 | 2021-08-10 | 深圳索信达数据技术有限公司 | Financial product recommendation method, electronic device and storage medium |
| CN114154075A (en) * | 2022-02-08 | 2022-03-08 | 北京大氪信息科技有限公司 | Hot information determination method, hot information determination device, computer equipment and medium |
| CN115131155A (en) * | 2022-05-11 | 2022-09-30 | 李益非 | Investment management based cloud platform and investment management method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7373324B1 (en) * | 1999-10-07 | 2008-05-13 | Robert C. Osborne | Method and system for exchange of financial investment advice |
| CN105117422A (en) * | 2015-07-30 | 2015-12-02 | 中国传媒大学 | Intelligent social network recommender system |
| CN107679239A (en) * | 2017-10-27 | 2018-02-09 | 天津理工大学 | Recommend method in a kind of personalized community based on user behavior |
| US20200357071A1 (en) * | 2014-09-05 | 2020-11-12 | Vested Interest Co. | System and method for personal investing |
-
2020
- 2020-12-29 CN CN202011596610.4A patent/CN112612942B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7373324B1 (en) * | 1999-10-07 | 2008-05-13 | Robert C. Osborne | Method and system for exchange of financial investment advice |
| US20200357071A1 (en) * | 2014-09-05 | 2020-11-12 | Vested Interest Co. | System and method for personal investing |
| CN105117422A (en) * | 2015-07-30 | 2015-12-02 | 中国传媒大学 | Intelligent social network recommender system |
| CN107679239A (en) * | 2017-10-27 | 2018-02-09 | 天津理工大学 | Recommend method in a kind of personalized community based on user behavior |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112966189A (en) * | 2021-04-14 | 2021-06-15 | 刘蒙 | Fund product recommendation system |
| CN112966189B (en) * | 2021-04-14 | 2024-01-26 | 北京基智科技有限公司 | Fund product recommendation system |
| CN113240472A (en) * | 2021-05-19 | 2021-08-10 | 深圳索信达数据技术有限公司 | Financial product recommendation method, electronic device and storage medium |
| CN113240472B (en) * | 2021-05-19 | 2024-02-02 | 深圳索信达数据技术有限公司 | Financial product recommendation method, electronic equipment and storage medium |
| CN114154075A (en) * | 2022-02-08 | 2022-03-08 | 北京大氪信息科技有限公司 | Hot information determination method, hot information determination device, computer equipment and medium |
| CN114154075B (en) * | 2022-02-08 | 2022-05-17 | 北京大氪信息科技有限公司 | Hot information determination method, device, computer equipment and medium |
| CN115131155A (en) * | 2022-05-11 | 2022-09-30 | 李益非 | Investment management based cloud platform and investment management method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112612942B (en) | 2022-09-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112612942B (en) | Social big data-based fund recommendation system and method | |
| CN111125453B (en) | Opinion leader role identification method in social network based on subgraph isomorphism and storage medium | |
| US11070643B2 (en) | Discovering signature of electronic social networks | |
| Courtois et al. | Farmer bargaining power and market information services | |
| CN111160992A (en) | Marketing system based on user portrait system | |
| US9703877B2 (en) | Computer-based evaluation tool for selecting personalized content for users | |
| Yang et al. | Friend or frenemy? Predicting signed ties in social networks | |
| CN102053971B (en) | Recommending method and equipment for sequencing-oriented collaborative filtering | |
| CN105117422A (en) | Intelligent social network recommender system | |
| US20120041769A1 (en) | Requests for proposals management systems and methods | |
| CN106296305A (en) | Electric business website real-time recommendation System and method under big data environment | |
| CN104254852A (en) | Method and system for hybrid information query | |
| Xu et al. | A novel approach to online social influence maximization | |
| US10191985B1 (en) | System and method for auto-curation of Q and A websites for search engine optimization | |
| Li | SWOT analysis of e‐commerce development of rural tourism farmers' professional cooperatives in the era of big data | |
| CN110245355B (en) | Text topic detection method, device, server and storage medium | |
| CN111695084A (en) | Model generation method, credit score generation method, device, equipment and storage medium | |
| Zhang | [Retracted] Optimization of the Marketing Management System Based on Cloud Computing and Big Data | |
| Wu et al. | Optimization matrix factorization recommendation algorithm based on rating centrality | |
| CN111429161A (en) | Feature extraction method, feature extraction device, storage medium, and electronic apparatus | |
| Deng et al. | Recommender system for marketing optimization | |
| CN118013120B (en) | Method, medium and equipment for optimizing products recommended to users based on cluster labels | |
| CN113449004A (en) | Data matching method and device | |
| CN114329167A (en) | Hyper-parameter learning, intelligent recommendation, keyword and multimedia recommendation method and device | |
| Li | Research on e-business requirement information resource extraction method in network big data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |