WO2012174648A1 - Exploration de données guidée par préférence et traitement sémantique - Google Patents

Exploration de données guidée par préférence et traitement sémantique Download PDF

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Publication number
WO2012174648A1
WO2012174648A1 PCT/CA2012/000603 CA2012000603W WO2012174648A1 WO 2012174648 A1 WO2012174648 A1 WO 2012174648A1 CA 2012000603 W CA2012000603 W CA 2012000603W WO 2012174648 A1 WO2012174648 A1 WO 2012174648A1
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WIPO (PCT)
Prior art keywords
user
preference
preferences
order
items
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Application number
PCT/CA2012/000603
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English (en)
Inventor
Peter J. SWEENEY
Ihab F. Ilyas
Mohamed A. SOLIMAN
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Primal Fusion Inc.
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Priority claimed from PCT/CA2012/000009 external-priority patent/WO2012092669A1/fr
Application filed by Primal Fusion Inc. filed Critical Primal Fusion Inc.
Priority to CA2841147A priority Critical patent/CA2841147C/fr
Priority to AU2012272479A priority patent/AU2012272479A1/en
Publication of WO2012174648A1 publication Critical patent/WO2012174648A1/fr
Priority to IL230065A priority patent/IL230065A/en
Priority to IL248313A priority patent/IL248313A/en
Priority to AU2017221807A priority patent/AU2017221807B2/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2457—Query processing with adaptation to user needs

Definitions

  • Information retrieval systems are capable of accessing enormous volumes of information. As a result, locating information of interest to users presents challenges. One such challenge is identifying information that may be of interest to users so that information may be presented to them without overwhelming users with irrelevant information. Even in environments, such as online search, where the user provides an explicit indication (e.g., a search query) of what information the user may be interested in, such an indication may not be sufficient to accurately identify the content which is appropriate to present to the user from among all the content that may be available to be presented to the user.
  • an explicit indication e.g., a search query
  • conventional approaches to identifying information of interest to a user often shift the burden of finding such information to the user.
  • conventional approaches to search may involve presenting all potentially relevant results to a user in response to the user's search query. Subsequently, the user has to manually explore and/or rank these results in order to find the information of greatest interest to him. When the number of potentially relevant results is large, which is often the case, the user may be overwhelmed and may fail to locate the information he is seeking.
  • One technique for addressing this problem is to integrate a user's preferences into the process of identifying information of interest to the user. By presenting information to the user in accordance with his preferences, the user may be helped to find the information he is seeking.
  • conventional approaches to specifying user preferences severely limit the ways in which user preferences may be specified, thereby limiting the utility of such approaches.
  • Query interface 12 is used to collect query predicates in the form of keywords and/or attribute values (e.g., "used Toyota" with price in the range [$2000-$5000]). Query results are then sorted (14) on the values of one or more attributes (e.g., order by Price then by Rating) in a major sort/minor sort fashion. The user then scans (16) through the sorted query answers to locate items of interest, refines query predicates, and repeats the exploration cycle (18).
  • This "Query, Sort, then Scan” model limits the flexibility of preference specification and imposes rigid information retrieval schemes, as highlighted in the following example.
  • Amy is searching online catalogs for a camera to buy. Amy is looking for a reasonably priced camera, whose color is preferably silver and less preferably black or gr a y > and whose reviews contain the keywords "High Quality.” Amy is a money saver, so her primary concern is satisfying her Price preferences, followed by her Color and Reviews preferences.
  • the data exploration model of FIG. 1 allows Amy to sort results in ascending price order. Amy then needs to scan through the results, which are sorted by price, comparing colors and inspecting reviews to find the camera that she wants.
  • the path followed by Amy to explore search results is mainly dictated by her price preference, while other preferences are incorporated in the exploration task through Amy's effort, which can limit the possibility of finding items that closely match her requirements.
  • preference specifications maybe inconsistent with one another.
  • a typical example is having cycles (or "circularity" in preferences among first-order preferences (preferences among attributes of items such as preferring one car to another car based on the price or on brand).
  • first-order preferences preferences among attributes of items such as preferring one car to another car based on the price or on brand.
  • a user may indicate that a Nissan is preferred to a Toyota, a Toyota is preferred to a Nissan, and a Nissan is preferred to a Nissan.
  • second-order preferences e.g., brand preferences are more important than price preferences
  • Conventional information retrieval systems are unable to rank search results when preference specifications may be inconsistent.
  • a computer-implemented method for calculating a ranking of at least one item in a plurality of items comprises receiving user preferences comprising a plurality of first-order user preferences indicative of a user's preferences for items in the plurality of items, and at least one second-order user preference indicative of the user's preferences among first-order user preferences in the plurality of first- order user preferences.
  • the method further comprises calculating, with at least one processor, a ranking of the at least one item in the plurality of items based, at least in part on, at least one data structure encoding a preference graph that represents the received user preferences, and identifying and outputting at least a subset of the plurality of items to a user, in accordance with the ranking.
  • a system comprising at least one memory configured to store a plurality of tuples, each tuple in the plurality of tuples corresponding to an item in a plurality of items, and at least one data structure encoding a preference graph to represent user preferences, wherein the user preferences comprise a plurality of first-order user preferences indicative of a user's preferences among items in the plurality of items, and at least one second-order user preference indicative of the user's preferences among first-order user preferences in the plurality of first-order user preferences.
  • the system further comprises at least one processor coupled to the at least one memory, the at least one processor configured to calculate a ranking of at least one item in the plurality of items based, at least in part on, the at least one data structure encoding the preference graph that represents the user preferences, and identify and output at least a subset of the plurality of items to a user, in accordance with the ranking.
  • At least one computer-readable storage medium article stores a plurality of processor-executable instructions that, when executed by at least one processor, cause the at least one processor to perform a method of calculating a ranking for at least one item in a plurality of items.
  • the method comprises receiving user preferences comprising a plurality of first-order user preferences indicative of a user's preferences among items in the plurality of items, and at least one second-order user preference indicative of the user's preferences among first-order user preferences in the plurality of first-order user preferences.
  • a computer-implemented method for constructing at least one data structure encoding a preference graph that represents user preferences comprises a first node for a first item in a plurality of items, a second node for a second item in the plurality of items, and an edge between the first node and the second node.
  • the method comprises receiving a plurality of first-order user preferences indicative of user preferences among values of attributes of items in the plurality of items, receiving at least one second-order user preference indicative of user preferences among the attributes of items in the plurality of items, and computing, using at least one processor, a weight for the edge between the first node and the second node based at least in part on the plurality of first-order user preferences and the at least one second-order user preference, wherein the weight is indicative of a degree of preference for the first item over the second item.
  • a system for constructing at least one data structure encoding a preference graph that represents user preferences comprising a first node for a first item in a plurality of items, a second node for a second item in the plurality of items, and an edge between the first node and the second node.
  • the system comprises at least on processor configured to receive a plurality of first-order user preferences indicative of user preferences among values of attributes of items in the plurality of items, receive at least one second-order user preference indicative of user preferences among the attributes of items in the plurality of items, and compute a weight for the edge between the first node and the second node based at least in part on the plurality of first-order user preferences and the at least one second- order user preference, wherein the weight is indicative of a degree of preference for the first item over the second item.
  • at least one computer-readable storage medium article is disclosed.
  • the method comprises receiving a plurality of first-order user preferences indicative of user preferences among values of attributes of items in the plurality of items, receiving at least one second-order user preference indicative of user preferences among the attributes of items in the plurality of items, and computing a weight for the edge between the first node and the second node based at least in part on the plurality of first-order user preferences and the at least one second-order user preference, wherein the weight is indicative of a degree of preference for the first item over the second item.
  • a system for obtaining user preferences comprises at least one processor configured to receive user context information associated with at least one user; identify, based at least in part on the received user context information, a plurality of attributes of items in a plurality of items; obtain, at least one first-order user preference based at least in part on a first input provided by the at least one user, wherein the plurality of first-order user preferences comprises a preference for a first attribute in the plurality of attributes; and obtain at least one second-order user preference based at least in part on a second input provided by the at least one user, wherein the at least one second-order user preference comprises a preference among attributes in the plurality of attributes.
  • At least one computer-readable storage medium article stores a plurality of processor-executable instructions that, when executed by at least one processor, cause the at least one processor to perform a method for obtaining user preferences.
  • At least one computer-readable storage medium article stores a plurality of processor-executable instructions that, when executed by at least one processor, cause the at least one processor to perform a method for specifying user preferences in a semantic network encoded in at least one data structure.
  • the method comprises receiving a plurality of first-order user preferences for at least one concept in a semantic network, wherein the plurality of first- order user preferences are indicative of a user's preferences among children of attributes of the at least one concept in the semantic network; receiving at least one second-order user preference for the at least one concept in the semantic network, wherein the at least one second-order user preference is indicative of the user's preferences among attributes of the at least one concept; and performing at least one semantic processing act by using the semantic network, the plurality of first-order user preferences, and the at least one second-order user preference.
  • FIG. 1 is a diagram of a "query, sort, then scan” data exploration model, in accordance with prior art.
  • FIG. 2A is a diagram illustrating a relation, in accordance with some embodiments of the present invention.
  • FIG. 2B is a diagram illustrating a semantic network associated with a portion of the relation illustrated in FIG. 2A.
  • FIG. 8 is a diagram of an illustrative preference graph, in accordance with some embodiments of the present invention.
  • FIG. 10 is a diagram of an illustrative page-rank based matrix for prioritized comparators, in accordance with some embodiments of the present invention.
  • FIG. 11 is a diagram of an illustrative weighted preference graph and tournaments derived from it, in accordance with some embodiments of the present invention.
  • a system capable of integrating different preference types and identifying information of interest to a user or users, in accordance with preferences specified by the user(s), may address some of the above-discussed drawbacks of conventional approaches to information retrieval. However, not every embodiment addresses every one of these drawbacks, and some
  • items may include metadata about content.
  • a user may prefer to see a webpage that contains information related to cars over a webpage that contains information related to bicycles.
  • a preference model may be used to identify information of interest to the user by ranking one or more of such items in accordance with any user preferences.
  • items may be represented, at least in part, by one or more concepts in a semantic network.
  • an item may be represented by a concept and one or more of its descendants.
  • an item may be represented by a concept, children of the concept, and grandchildren of the concept.
  • an item may be represented by any entity or entities in a semantic network as aspects of the present invention are not limited in this respect.
  • the preference model may be used to produce a ranking of one or more concepts in a semantic network or the preference may be used for this purpose in any other suitable way.
  • the preference model may be a graph-based preference model and the data structure encoding the preference model may encode a graph, termed a preference graph, characterizing the graph-based preference model.
  • the preference graph may comprise a set of nodes (vertices) and a set of edges connecting nodes in the set of nodes. The edges may be directed edges or may be undirected edges.
  • the data structure encoding the preference graph may encode the preference graph by encoding the graph's vertices and edges. Any of numerous data structures for encoding graphs, as are known in the art, may be used to encode the preference graph, as the invention is not limited in this respect.
  • a first-order preference for one item over another item may be represented as an edge in the preference graph, with the edge connecting nodes associated with the tuples associated with the two items.
  • a weight may be associated to each edge in the preference graph to provide an indication of a degree of preference for one of the nodes terminating the edge. The weight may be computed based on first-order and/or second preferences. Aspects of a graph-based preference model, including how such a preference model may be constructed from user-specified preferences, are described in greater detail in Sections IV and VII, below.
  • a semantic network and/or other knowledge representation may be implemented on a physical system via at least one data structure that may encode the semantic network and/or other knowledge representation.
  • data structures and processor-executable instructions may be encoded on any suitable tangible computer-readable storage medium article or articles.
  • Such data structures provide a physical instantiation in which a physical memory holds information organized according to certain rules to facilitate use of the information by any software program that assumes such organization.
  • Software modules comprising stored program instructions may be provided to cause one or more processors to perform any of numerous of tasks in accordance with some of the disclosed embodiments. For example, one or multiple software modules for constructing a preference model may be provided. As another example, software modules for obtaining a ranking for a set of items based on (a data structure representing) the preference model may be provided. As another example, software modules comprising instructions for implementing any of numerous functions associated with an information retrieval system may be provided. Though, it should be recognized that the above examples are not limiting and software modules may be provided to perform any functions in addition to or instead of the above examples.
  • the system may assist a user to specify preferences.
  • such support may be based on pre-computed summaries, termed "facets," that may be used for guiding information retrieval.
  • facets may be associated with a number that may provide the user with an estimate on the expected number of results. Accordingly, facets may allow a user to get a quick and dirty view of the underlying set of items and/or domain, and how search results may be affected by tuning preferences.
  • the system may comprise a memory 302 configured to store a plurality of tuples (recall that each tuple comprises one or more values for one or more attributes) and may receive a range of desired values for an attribute from a user. In response, the system may output a value indicative of a number of tuples comprising a value for the attribute such that the value is in the range of values.
  • a facet may comprise a range of possible values (e.g., 'Price in the range [$1000-$5000]').
  • a context for expressing first-order preferences termed a "scope,” may be defined in accordance with the following definition:
  • scopes may intersect.
  • a tuple in the relation R may belong to zero, one or two or more scopes. Tuples that do not belong to any scopes may be non-interesting with respect to a preference specification. Thus, for clarity, all subsequent discussion is with respect to tuples that belong to at least one scope.
  • the scope comparator fi j is a function that takes a pair of distinct tuples (one is from Ri and the other is from R j ), and returns a first value such as 1 (e.g., if the tuple from Ri is preferred), a second value such as -1 (e.g., the tuple from R j is preferred), or a null value "J- " (e.g., if there is no preference).
  • FIG. 5 shows illustrates 5 different scope comparators defined on the scopes shown in FIG. 4.
  • the scope comparators fs ⁇ and f ⁇ are unconditional (i.e., they produce first- order preferences without testing any conditions beyond the conditions captured by scope definition).
  • the scope comparators f t 2 , fs,6 , fs,2 are conditional ( produce preference relations conditioned on some logic).
  • a total order on a scope R t (which can be the whole relation R) may be encoded by defining a comparator f , using the template in Algorithm 1 , where f operates on pairs of distinct tuples belonging to Rj.
  • P x be a partial order defined on the domain of x.
  • Partial order-based preferences may be encoded using the template given by Algorithm 2.
  • F be the set of all scope comparators associated with the preference graph.
  • A be the set of POrders of F according to the chosen semantics of second-order preferences.
  • A be the multiset of nonempty projections
  • FIG. 9 shows three weighted preference graphs, corresponding to the preference graph in FIG. 8, produced under different semantics of second-order preferences.
  • the different semantics of second-order preferences result in different edge weights and/or the removal of some edges in the original preference graph:
  • entries in ⁇ are normalized so that they sum to 1.0.
  • the number of iterations needed for the power method to converge may be any suitable of iterations. For instance, tens or hundreds of iterations may be used.
  • FIG. 10 illustrates the pagerank matrix for the weighted preference graph with prioritized comparators illustrated in FIG. 9.
  • U is a sink node with no incoming edges (i.e., ⁇ has no other dominating tuples).
  • ⁇ has no other dominating tuples.
  • a typical value of the damping factor a may be a value such as 0.15, but may be any value between 0 and 0.5.
  • the value of the weight w' (J represents the probability of selecting a POrder, among the set of all POrders relevant to ⁇ t tj), under which ⁇ We thus interpret Wij as the probability with which tuple t, is preferred to tuple t j .
  • the operation add edge adds one of the edges e t - j or e j j with the same probability 0.5.
  • An information retrieval system may obtain user preferences in various ways.
  • the information retrieval system may interact with one or more users to obtain user preferences.
  • the system may interact with the user(s) to obtain first-order preferences and/or second-order preferences and may interact with the user(s) in any suitable way to obtain these preferences.
  • the system may present any suitable information or interface to the user(s) to assist the user(s) in specifying preferences.
  • the information retrieval system may obtain some, or even all, user preferences without interacting with the user and, for example, may simply receive user preferences from another source and/or utilize user preferences previously obtained by the system or determined "passively," or implicitly, as by observing user behavior.
  • user context information associated with one or more users may comprise information provided by the user. Such information may be any suitable information indicative of what information the user may be interested in.
  • user context information may comprise one or more user search queries input by a user into a search engine (e.g., an Internet search engine, a search engine adapted for searching a particular domain such as a corporate intranet, etc.).
  • a search query may comprise one or more keywords.
  • the search query may be a query containing the keyword "car” and may indicate that a user may be interested in looking at items related to cars.
  • the user may input a query "television" into an Internet search engine, which may indicate that a user may be interested in looking at any webpages containing information about television.
  • a user may be presented with a list of previously mentioned attributes associated with the keyword "car” and may select the attributes "Price” and "Color.”
  • attributes selected by the user may be received.
  • the user may select one or more attributes in any suitable way by providing any of numerous types of input including, but not limited to, using a mouse click (e.g., to check a checkbox, to click a button, selecting an area of the screen, etc.), dragging an item on the screen, pressing a button on a keyboard, etc.
  • the user's selection is received in act 1208. Though, it should be appreciated that, aspects of the present invention are not limited to selecting attributes by interacting with a user and, in some embodiments, attributes may be selected automatically.
  • process 1300 proceeds to act 1308, where one or more weights for the preference graph may be computed.
  • a weight may be associated to each of one or more edges in the preference graph in order to provide an indication of a degree of preference for one of the nodes terminating the edge.
  • the weight may be computed based on first-order and/or second-order preferences.
  • the weight may be computed in any of the ways described in Section IV above or in any other suitable way.
  • Output associated with an item may comprise any suitable information about or related to the item.
  • output associated with an item may comprise one or more values of attributes of the item.
  • output associated with each car may comprise one or more attribute values (e.g., "price,” "color,” etc.) of that car.
  • output associated with an item may comprise information identifying the item.
  • output associated with each car may comprise an identifier of that car.
  • first-order preferences may include preferences among children of an attribute, for each of the multiple attributes of the concept (e.g., first-order preferences specified for children of the attribute "Price” and first-order preferences specified for children of the attribute "Color").
  • exemplary system 1700 may include a preference engine 1702.
  • synthetical components 1752 may comprise preference engine 1702.
  • preference engine 1702 may receive context information 180 containing preference information.
  • the preference information may comprise a preference model.
  • preference engine 1702 may create a preference model based on the preference information.
  • preference engine 1702 may provide preference information and/or a preference model to synthesis engine 170.
  • synthesis engine 170 may rely on the preference information and/or the preference model provided by preference engine 1702 to guide synthesis of a complex KR in accordance with preferences of a data consumer 195.
  • preference engine 1702 may rely on preference information and/or the preference model to guide presentation of concepts in a complex KR and/or presentation of output KRs in accordance with preferences of a data consumer 195.
  • inventive concepts may be embodied as at least one non-transitory computer-readable storage medium (e.g., a computer memory, one or more floppy discs, compact discs, optical discs, magnetic tapes, flash memories, circuit configurations in Field Programmable Gate Arrays or other semiconductor devices, etc.) article(s) encoded with one or more programs that, when executed on one or more computers or other processors, implement the various process embodiments of the present invention.
  • the non-transitory computer-readable medium or media may be transportable, such that the program or programs stored thereon may be loaded onto any suitable computer resource to implement various aspects of the present invention as discussed above.
  • inventive concepts may be embodied as one or more methods, of which an example has been provided.
  • the acts performed as part of the method may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments, or vice versa. All definitions, as defined and used herein, should be understood to control over dictionary definitions, definitions in documents incorporated by reference, and/or ordinary meanings of the defined terms.
  • At least one of A and B can refer, in one embodiment, to at least one, optionally including more than one, A, with no B present (and optionally including elements other than B); in another embodiment, to at least one, optionally including more than one, B, with no A present (and optionally including elements other than A); in yet another embodiment, to at least one, optionally including more than one, A, and at least one, optionally including more than one, B (and optionally including other elements); etc.

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Abstract

L'invention concerne des techniques pour calculer un classement d'au moins un article dans une pluralité d'articles. Les techniques consistent à recevoir des préférences d'utilisateur comprenant une pluralité de préférences d'utilisateur de premier ordre indicatives de préférences d'un utilisateur pour des articles dans la pluralité d'articles, et au moins une préférence d'utilisateur de second ordre indicative de préférences de l'utilisateur parmi des préférences d'utilisateur de premier ordre dans la pluralité de préférences d'utilisateur de premier ordre ; à calculer, à l'aide d'au moins un processeur, un classement du ou des articles dans la pluralité d'articles sur la base, au moins en partie, d'au moins une structure de données codant un graphique de préférences qui représente les préférences d'utilisateur reçues ; et à identifier et à émettre au moins un sous-ensemble de la pluralité d'articles à un utilisateur, conformément au classement.
PCT/CA2012/000603 2011-06-20 2012-06-20 Exploration de données guidée par préférence et traitement sémantique WO2012174648A1 (fr)

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CA2841147A CA2841147C (fr) 2011-06-20 2012-06-20 Exploration de donnees guidee par preference et traitement semantique
AU2012272479A AU2012272479A1 (en) 2011-06-20 2012-06-20 Preference-guided data exploration and semantic processing
IL230065A IL230065A (en) 2011-06-20 2013-12-19 Research on data-driven preference and semantic processing
IL248313A IL248313A (en) 2011-06-20 2016-10-11 Research on data-driven preference and semantic processing
AU2017221807A AU2017221807B2 (en) 2011-06-20 2017-08-30 Preference-guided data exploration and semantic processing

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PCT/CA2012/000009 WO2012092669A1 (fr) 2011-01-07 2012-01-06 Systèmes et procédés pour analyser et synthétiser des représentations de connaissances complexes

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CA2841147C (fr) 2022-03-01
IL248313A (en) 2017-07-31
CA2841147A1 (fr) 2012-12-27
IL230065A (en) 2016-10-31
AU2012272479A1 (en) 2014-01-16
AU2017221807A1 (en) 2017-09-21
WO2012174632A1 (fr) 2012-12-27
AU2017221807B2 (en) 2019-07-18

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