Heterogeneous Graph-Based Intent Learning with Queries, Web Pages and Wikipedia Concepts

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1 Heterogeneous Graph-Based Intent Learning with Queries, Web Pages and Wikipedia Concepts Xiang Ren, Yujing Wang, Xiao Yu, Jun Yan, Zheng Chen, Jiawei Han University of Illinois, at Urbana Champaign MicrosoD Research 02/25/2014

2 User Search Intents Query understanding: Distinguish ambiguous or multi-faceted queries apple à company or fruit? MS office à download or FAQ? Guide users to vertical search engines Travel, cars, news, movies, sports Search result presentation: Organize search results w.r.t. different search intents Search result diversification x Refine search results based on search intent Intent-aware re-ranking of search results

3 Take advantages of heterogeneous types of data Basic idea: Combine query, web page and Wiki information to learn generic search intents Complement each other to enhance performance Reduce drawbacks from using a single type of data Assist intent interpretation by Wiki concepts Most of the previous studies only use single type of data: query text (Zeng, SIGIR 04), search log (Hu, WWW 05, Radlinski, WWW 10), name entities (Hu, WWW 05) or Wiki concepts (Bordino, WSDM 13). Combine search log with query text (Hu SIGIR 12), or with session data (Sadikov, WWW 10)

4 Heterogeneous Graph for Intent Learning Encode multiple data sources into a unified graph representation page-page relation page-concept relation query-page relation p 1 p 2 text-based similarity 0.47 p 3 q 1 q 2 q 3 click count ESA score 0.07 p 4 web page query e 3 e 2 e 1 concept Link weight denotes object similarity in terms of search intents

5 The Proposed Method Graph-based soft-clustering of all objects (query, web page, wiki concept) Intent A Intent B Intent C Intent D Suppose there are k search intents, define intent indicators: Given the heterogeneous graph, learn all intent indicators simultaneously

6 Problem Formulation Heterogeneous graph-based soft-clustering (HSoC) Local consistency on graph (Belkin, NIPS 01): linked objects tend to share similar search intent larger link weights à more similar intent indicators Text-based Web page relations Query-page relations Concept-based Web page relations Soft-cluster indicator constraints

7 The Learning Algorithm A two-stage approximate algorithm: Learn latent features for each object by heterogeneous graph embedding Apply fuzzy c-means to latent features to get intent indicators Heterogeneous graph embedding (HGE): Solve by k generalized eigenvectors corresponding to the k smallest eigenvalues of above eigen-problem (He, NIPS 03)

8 Experimental Setups Datasets Two applications: Search ranking (query-page similarity in terms of search intents) Object co-clustering (queries, web pages and concepts) Evaluation sets Manually judge a subset of query-page pairs and a subset of queryconcept pairs: Perfect, Excellent, Good, Fair and Bad (0-4)

9 Search Ranking: Performance Comparisons Compared methods HGE-CG: only consider page content and click graph HGE-EG: only consider click graph and wiki concepts HGE-Fea: integrate linear model into intent indicators State-of-the-art methods BM25 Random walk (Craswell, SIGIR 07) MCoC (Sadikov,WWW 10): Multiclass co-clustering on click graph M-PLS (Wu, WSDM 13): Multi-view partial least square using click graph and object features

10 Object Co-Clustering: Case Study

11 Conclusion and Future Work Conclusion A novel search intent definition utilizing three types of objects A unified framework for joint intent learning A heterogeneous graph to encode all information A graph-based soft-clustering method Better performance of intent learning, easy to interpret intents Future work Extend to automatically learning relation importance Online updating of the model

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