WO2017118333A1 - 一种基于数据驱动预测用户问题的方法及装置 - Google Patents
一种基于数据驱动预测用户问题的方法及装置 Download PDFInfo
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- 238000004364 calculation method Methods 0.000 description 2
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- the invention belongs to the technical field of data processing, and in particular relates to a method and device for predicting user problems based on data driving.
- Predicting problems that users may encounter in advance is a typical multi-classification problem, usually consisting of two parts: feature selection and model modeling.
- feature selection end extracts features
- it is usually artificially set by rules, which are empirically considered to be related to problems that the user may ask, such as whether the user has opened a certain service, in the past few Have you ever had a consumption record in the day?
- rules which are empirically considered to be related to problems that the user may ask, such as whether the user has opened a certain service, in the past few Have you ever had a consumption record in the day?
- behaviors include, but are not limited to, mobile phones, tablet client clicks, PC web browsing, and other operations performed by the user. This includes the user's behavioral trajectory information before the question. In theory, these behavioral trajectories are strongly related to the user's subsequent help. .
- predicting the problems encountered by the user as accurately as possible before the user describes the problem can avoid the influence of the prior art human intervention and improve the accuracy of the classification prediction.
- a method for predicting user problems based on data driving includes:
- the selected behavior data is filtered by the set target behavior data set, the candidate behavior data included in the target behavior data set is filtered out from the candidate behavior data, and the selected candidate behavior data is input into the trained classifier model. Predict the category to which the user’s question belongs.
- the trained classifier model includes the following steps:
- the data-driven method is used to score the candidate behavior data corresponding to each user's feedback, and the target behavior data meeting the set conditions is filtered out.
- the target behavior data corresponding to the user feedback problem is a union of the target behavior data set by the screening;
- the classifier model is trained according to the feedback of each user and the target behavior data set.
- the preprocessing includes:
- the interference behavior data whose frequency is lower than the set frequency threshold is removed.
- the preprocessing further includes:
- the behavioral data is digitally identified in order to facilitate the processing of the digital identification directly in the subsequent steps, so that it is not necessary to process according to the specific data of the behavior data, such as a long string data such as a web address or an API name, and the processing is simpler.
- the present invention intercepts the candidate behavior data that contributes to the problem raised by the user from the pre-processed user behavior data by using windowing truncation, and the windowing truncation includes:
- the method further includes:
- the method further includes the steps of:
- the target behavior data in the target behavior data set is vectorized.
- the method further includes:
- the selected behavior data is vectorized.
- the vectorized user behavior data can directly train the classifier model and be used for actual prediction, making calculations easier.
- the present invention also provides an apparatus for predicting user problems based on data driving, and the apparatus for predicting user problems includes:
- a pre-processing module configured to collect user behavior data and perform pre-processing when receiving a question raised by the user
- An intercepting module configured to intercept, from the pre-processed user behavior data, candidate behavior data that contributes to a problem raised by the user;
- the prediction module is configured to filter the selected behavior data by the set target behavior data set, select the candidate behavior data included in the target behavior data set from the candidate behavior data, and input the selected candidate behavior data into the training.
- the classifier model predicts the category to which the user's question belongs.
- the apparatus further includes a model training module for training the classifier model, and the model training model performs the following operations when training the classifier model:
- the data-driven method is used to score the candidate behavior data corresponding to each user's feedback, and the target behavior data meeting the set conditions is filtered out.
- the target behavior data corresponding to the user feedback problem is a union of the target behavior data set by the screening;
- the classifier model is trained according to the feedback of each user and the target behavior data set.
- the preprocessing module of the present invention performs the following steps when preprocessing the collected user behavior data:
- the interference behavior data whose frequency is lower than the set frequency threshold is removed.
- the pre-processing module is further configured to digitally identify user behavior data.
- the windowing truncation method is adopted, and the windowing truncation includes:
- model training module is configured to re-mark the target behavior data in the target behavior data set after the aggregated target behavior data corresponding to the feedback of all the users constitutes the filtered target behavior data set.
- model training module is further configured to perform vectorization processing on the target behavior data in the target behavior data set before training the classifier model.
- the prediction module further performs vectorization processing on the selected behavior data before inputting the selected candidate behavior data into the trained classifier model.
- the invention provides a method and device for predicting user problems based on data driving, and uses the behavior track information of the user in a short time to classify and predict user problems to improve the classification accuracy rate, and significantly improve the model prediction effect without including such information.
- FIG. 1 is a flow chart of a training classifier model of the present invention
- FIG. 2 is a flow chart of a method for predicting a user problem based on data driving according to the present invention
- FIG. 3 is a schematic structural diagram of an apparatus for predicting a user problem based on data driving according to the present invention.
- the general idea of the present invention is to train the classifier model using the training data, and analyze the user behavior data according to the trained classifier model to predict the problems encountered by the user.
- the process of training the classifier model by using the training data in this embodiment is as follows:
- F1 Collecting user feedback problems and corresponding behavior data, preprocessing the collected user behavior data, preprocessing includes removing interference behavior data, and digitally identifying behavior data.
- Behavioral data is a number of user actions, including mobile phones, tablet client clicks, PC web browsing, and other operations performed by the user, represented by a URL or API name, preceded by a unix timestamp.
- Behavioral data is a number of user actions, including mobile phones, tablet client clicks, PC web browsing, and other operations performed by the user, represented by a URL or API name, preceded by a unix timestamp.
- the behavior of a user X in the past period of time can be expressed as:
- the preprocessing of this embodiment includes removing interference behavior data and digitally identifying behavior data.
- the removal of the interference behavior data refers to the behavior data that is extremely low in frequency, for example, lower than the set frequency threshold. These behavior data with extremely low frequency result in the possibility of user feedback is relatively low, and this embodiment does not consider, thereby eliminating the interference caused by the behavior data with extremely low frequency.
- the behavioral data is digitally identified in order to facilitate the direct processing of the digitized identifier in the subsequent steps, so that it is not necessary to process according to the specific data of the behavior data, such as a long string data such as a web address or an API name, and the processing is simpler.
- the URL or API of the above behavior data may be digitally identified according to the mapping table prepared in advance; or by counting the frequency of occurrence of the behavior data, the number is sorted according to the frequency quantity, and the number is used.
- a digital identifier of the behavior data or according to the specific content of the behavior data, the corresponding digitized identifier is obtained through HASH calculation.
- the behavior data after the digital identification becomes:
- the digitization identification is used for screening and processing directly in subsequent steps.
- the actual feedback to the user feedback problem is often the behavior data of the user in the most recent period before the problem occurs. That is, the behavior data contributing to the feedback of the user is the behavior data of the user in the most recent time, and the historical behavior data can ignore the influence. Therefore, this embodiment needs to intercept user behavior data.
- the behavior data of the user's most recent time is selected as the candidate behavior data.
- a fixed window length or a variable window length can be selected.
- the fixed window length is, for example, 30-120 behavior data, that is, 30-120 behavior data are selected from the current behavior data;
- the variable window length is behavior data that is selected from the current behavior data for a certain period of time, for example, the current time is 0.5. Behavioral data within hours - 2 hours.
- windowing is truncated from the last behavior data, ie, 1436862999:111, back length, fixed length window length (30-120 data) or variable window length (0.5 hours - 2 hours, through Unix The timestamp is determined). Assume that the data becomes truncated by windowing:
- the candidate behavior data contributing to the feedback of the user is obtained, and the behavior data corresponding to the problem fed back by each user is traversed, and the candidate behavior data corresponding to the problem fed back by each user is obtained.
- the data-driven method is used to score the candidate behavior data corresponding to each user feedback problem, and the target behavior data meeting the set conditions is selected.
- the target behavior data corresponding to the feedback of all users is combined to form a filtered target behavior data set.
- This embodiment takes all the feedbacks from the user as a file set, and each user's feedback is a file.
- the data-driven method in this embodiment is a TF-IDF method, which is a commonly used weighting technique statistical method for information retrieval and data mining, for evaluating a word for a file set or a corpus.
- the importance of a document The importance of a word increases proportionally with the number of times it appears in the file, but it also decreases inversely with the frequency it appears in the corpus.
- the words are equivalent to the candidate behavior data, and all the user feedback questions are used as a file set, and each user feedback problem is used as a file, and the question corresponding to each user feedback through TF-IDF is selected.
- the behavioral data is scored.
- TF-IDF The main idea of TF-IDF is: If a word or phrase appears in an article with a high frequency TF and rarely appears in other articles, then the word or phrase is considered to have good class distinguishing ability and is suitable for use. To classify. TFIDF is actually: TFXIDF, TF word frequency (Term Frequency), IDF inverse document frequency (Inverse Document Frequency). TF indicates the frequency at which the entry appears in document d.
- IDF The main idea of IDF is: if there are fewer documents containing the term t, that is, the smaller n is, the larger the IDF is, indicating that the term t has a good class distinguishing ability. If the number of documents containing a term t in a certain class C.
- the first step is to calculate the word frequency.
- the inverse document frequency is calculated.
- the third step is to calculate the TF-IDF.
- TF-IDF Word Frequency (TF) ⁇ Reverse Document Frequency (IDF)
- User behavior data can be regarded as words to some extent.
- the importance of words to a file set or one of the files in a corpus is used by TF-IDF technology to filter and filter behavior data.
- the behavior data suitable for classification is referred to as the target behavior data.
- the behavior data of the highest N (50-200) or higher than the certain threshold is scored as the target behavior data by the problem of feedback from each user.
- the target behavior data corresponding to the feedback of all users is combined to form a filtered target behavior data set, and the set contains the behavior data much less than the behavior data in all the training data.
- the behavior data corresponding to question A is (indicated by a digitized identifier):
- the union of the digital identification sets corresponding to all the questions constitutes the target behavior data set. It can be seen that when the above-mentioned user feedback problem is identified as a known problem, the target behavior data set contains the target behavior data of all known problems. .
- target behavior data in the target behavior data set is re-digitized, so that the collection is simpler and convenient for subsequent processing.
- the classifier model is trained according to the problem of each user feedback and the target behavior data set.
- Classifier models include, but are not limited to, logistic regression models, deep neural network models, support vector machine models, recursive neural network models, and the like. In view of the prior art, there are many methods for obtaining a model based on training data, and details are not described herein again.
- this embodiment is a method for predicting user problems based on data driving, including:
- Step S1 When receiving a question raised by the user, collect user behavior data and perform pre-processing.
- Step S2 intercepting behavior data that contributes to the problem fed back by the user from the pre-processed user behavior data as the candidate behavior data.
- the customer service After the customer service receives the user's question, it can grab the user behavior data for pre-processing.
- the specific method of pre-processing and how to perform windowing truncation have been described in the above training classifier model, and will not be described here.
- Step S3 screening the selected behavior data by the set target behavior data set, selecting the candidate behavior data included in the target behavior data set from the candidate behavior data, and inputting the selected candidate behavior data into the trained classification.
- the model predicts the category to which the user's question belongs.
- user X's behavior data becomes filtered through the target behavior data set:
- the three pieces of data will be removed because they are not included in the target behavior data set.
- the classifier model Since the target behavior data set has been obtained through screening, the classifier model is trained. Therefore, when a user submits a question to the customer service, the customer service can submit the user's candidate behavior data to the trained classifier model, and the classifier model calculates which type of problem the user asks, and the output corresponds to different The probability of the problem, the question with the highest probability of selection as the category to which the user's question belongs.
- the present embodiment is a method for predicting user problems based on data driving, and separately performs vectorization processing on the target behavior data in the target behavior data set, and the behavior to be selected.
- the data is vectorized.
- Binarization refers to the position of the corresponding vector position, and does not appear to be set to 0; the quantization refers to the number of occurrences of this behavior at the corresponding vector position.
- the vectorized user behavior data can directly train the classifier model and be used for actual prediction, or it can be combined with the original features to train the classifier model and used for actual prediction.
- the present invention also provides an apparatus for predicting user problems based on data driving, the apparatus comprising:
- a pre-processing module configured to collect user behavior data and perform pre-processing when receiving a question raised by the user
- An intercepting module configured to intercept, from the pre-processed user behavior data, candidate behavior data that contributes to a problem raised by the user;
- the prediction module is configured to filter the selected behavior data by the set target behavior data set, select the candidate behavior data included in the target behavior data set from the candidate behavior data, and input the selected candidate behavior data into the training.
- the classifier model predicts the category to which the user's question belongs.
- the apparatus for predicting a user problem in this embodiment further includes a model training module for training the classifier model, and the model training model performs the following operations when training the classifier model:
- the data-driven method is used to score the candidate behavior data corresponding to each user's feedback, and the target behavior data meeting the set conditions is filtered out.
- the target behavior data corresponding to the user feedback problem is a union of the target behavior data set by the screening;
- the classifier model is trained according to the feedback of each user and the target behavior data set.
- the interference behavior data whose frequency is lower than the set frequency threshold is removed.
- the pre-processing module is further configured to digitally identify user behavior data.
- the window truncation method when the candidate behavior data contributing to the problem raised by the user is intercepted from the pre-processed user behavior data, the window truncation method is adopted, and the window truncation includes:
- the target behavior of the model training module of the present embodiment corresponding to the feedback of all users After the data is combined to form the filtered target behavior data set, it is also used to digitally identify the target behavior data in the target behavior data set.
- the model training module of the embodiment is further used for vectorizing the target behavior data in the target behavior data set before training the classifier model.
- the prediction module of the embodiment is further used for vectorizing the selected behavior data before inputting the selected candidate behavior data into the trained classifier model.
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Claims (16)
- 一种基于数据驱动预测用户问题的方法,其特征在于,所述预测用户问题的方法包括:当收到用户提出的问题时,采集用户行为数据并进行预处理;从预处理后的用户行为数据中截取对用户提出的问题有贡献的待选行为数据;通过设定的目标行为数据集合对待选行为数据进行筛选,从待选行为数据中筛选出目标行为数据集合包含的待选行为数据,将筛选出的待选行为数据输入训练好的分类器模型,预测出用户提出的问题所属的类别。
- 根据权利要求1所述的基于数据驱动预测用户问题的方法,其特征在于,所述训练好的分类器模型,训练过程包括如下步骤:采集用户反馈的问题及其对应的行为数据,对采集的用户行为数据进行预处理;从预处理后的用户行为数据中截取对用户反馈的问题有贡献的行为数据作为待选行为数据;根据所有用户反馈的问题及其对应的待选行为数据,采用数据驱动的方法对每一个用户反馈的问题对应的待选行为数据进行打分,并筛选出符合设定条件的目标行为数据,对所有用户反馈的问题对应的目标行为数据取并集构成筛选出的目标行为数据集合;根据每一个用户反馈的问题及目标行为数据集合,训练得到分类器模型。
- 根据权利要求1或2所述的基于数据驱动预测用户问题的方法,其特征在于,所述预处理包括:去除频次低于设定的频次阈值的干扰行为数据。
- 根据权利要求3所述的基于数据驱动预测用户问题的方法,其特征在于,所述预处理还包括:对用户行为数据进行数字化标识。
- 根据权利要求1或2所述的基于数据驱动预测用户问题的方法,其特征在于,所述从预处理后的用户行为数据中截取对用户提出的问题有贡献的待选行为数据采用加窗截断的方法,所述加窗截断包括:截取在发生问题前最近一段时间内的用户行为数据。
- 根据权利要求4所述的基于数据驱动预测用户问题的方法,其特征在于,所述对所有用户反馈的问题对应的目标行为数据取并集构成筛选出的目标行为数据集合之后,还包括:重新对目标行为数据集合中的目标行为数据进行数字化标识。
- 根据权利要求2所述的基于数据驱动预测用户问题的方法,其特征在于,所述训练得到分类器模型之前,还包括步骤:对目标行为数据集合中的目标行为数据进行矢量化处理。
- 根据权利要求7所述的基于数据驱动预测用户问题的方法,其特征在于,所述将筛选出的待选行为数据输入训练好的分类器模型之前,还包括:对待选行为数据进行矢量化处理。
- 一种基于数据驱动预测用户问题的装置,其特征在于,所述预测用户问题的装置包括:预处理模块,用于当收到用户提出的问题时,采集用户行为数据并进行预处理;截取模块,用于从预处理后的用户行为数据中截取对用户提出的问题有贡献的待选行为数据;预测模块,用于通过设定的目标行为数据集合对待选行为数据进行筛选,从待选行为数据中筛选出目标行为数据集合包含的待选行为数据,将筛选出的待选行为数据输入训练好的分类器模型,预测出用户提出的问题所属的类别。
- 根据权利要求9所述的基于数据驱动预测用户问题的装置,其特征在于,所述装置还包括模型训练模块,用于训练分类器模型,所述模型训练模型在训练分类器模型时,执行如下操作:采集用户反馈的问题及其对应的行为数据,对采集的用户行为数据进行预处理;从预处理后的用户行为数据中截取对用户反馈的问题有贡献的行为数据作为待选行为数据;根据所有用户反馈的问题及其对应的待选行为数据,采用数据驱动的方法对每一个用户反馈的问题对应的待选行为数据进行打分,并筛选出符合设定条件的目标行为数据,对所有用户反馈的问题对应的目标行为数据取并集构成筛选出的目标行为数据集合;根据每一个用户反馈的问题及目标行为数据集合,训练得到分类器模型。
- 根据权利要求9或10所述的基于数据驱动预测用户问题的装置,其特征在于,所述预处理模块在对采集的用户行为数据进行预处理时,执行如下步骤:去除频次低于设定的频次阈值的干扰行为数据。
- 根据权利要求11所述的基于数据驱动预测用户问题的装置,其特征在于,所述预处理模块还用于对用户行为数据进行数字化标识。
- 根据权利要求9或10所述的基于数据驱动预测用户问题的装置,其特征在于,所述截取模块在从预处理后的用户行为数据中截取对用户提出的问题有贡献的待选行为数据时,采用加窗截断的方法,所述加窗截断包括:截取在发生问题前最近一段时间内的用户行为数据。
- 根据权利要求12所述的基于数据驱动预测用户问题的装置,其特征在于,所述模型训练模块对所有用户反馈的问题对应的目标行为数据取并集构成筛选出的目标行为数据集合之后,还用于重新对目标行为数据集合中的目标行为数据进行数字化标识。
- 根据权利要求10所述的基于数据驱动预测用户问题的装置,其特征在于,所述模型训练模块在训练得到分类器模型之前,还用于对目标行为数据集合中的目标行为数据进行矢量化处理。
- 根据权利要求15所述的基于数据驱动预测用户问题的装置,其特征在于,所述预测模块在将筛选出的待选行为数据输入训练好的分类器模型之前,还用于对待选行为数据进行矢量化处理。
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JP2018535292A JP2019505909A (ja) | 2016-01-08 | 2016-12-29 | ユーザ質問を予測するためのデータ駆動型方法及び装置 |
US16/029,508 US11481698B2 (en) | 2016-01-08 | 2018-07-06 | Data-driven method and apparatus for handling user inquiries using collected data |
US18/045,801 US11928617B2 (en) | 2016-01-08 | 2022-10-11 | Data-driven method and apparatus for handling user inquiries using collected data |
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US11481698B2 (en) | 2022-10-25 |
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EP3401853A1 (en) | 2018-11-14 |
US11928617B2 (en) | 2024-03-12 |
EP3401853A4 (en) | 2019-01-23 |
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