Automatic task-based profile representation for content-based recommendation

This paper proposes task-oriented content-based recommendation for cross service recommendation. The proposed method has two features: one is that task-based features are automatically mined from the web, second is that it estimates user's intention on task, which means "what user wants to do" and "what problem user has" by task-based profile representation. We evaluate improvement of recommendation accuracy by user evaluation, in which we collect ratings on variety of contents i.e. mobile web content, TV programs, restaurants, sightseeing spots, and hotels from 1,859 people and conduct cross validation. In an experiment, the combination of task-based profile representation and term-based representation yields a 17.7% improvement in MAE Mean Absolute Error compared to term-based profile representation only or domain content category based profile representation only.

[1]  Susan Gauch,et al.  Improving Ontology-Based User Profiles , 2004, RIAO.

[2]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[3]  Fabrizio Silvestri,et al.  Identifying task-based sessions in search engine query logs , 2011, WSDM '11.

[4]  Thorsten Joachims,et al.  Optimizing search engines using clickthrough data , 2002, KDD.

[5]  Lars Schmidt-Thieme,et al.  Taxonomy-driven computation of product recommendations , 2004, CIKM '04.

[6]  J. Jenkins,et al.  Word association norms , 1964 .

[7]  R. Likert.  “Technique for the Measurement of Attitudes, A” , 2022, The SAGE Encyclopedia of Research Design.

[8]  J. R. Firth,et al.  A Synopsis of Linguistic Theory, 1930-1955 , 1957 .

[9]  Gerhard Weikum,et al.  Task-aware search personalization , 2008, SIGIR '08.

[10]  J. Marden.  Analyzing and Modeling Rank Data , 1996 .

[11]  Kenneth Ward Church,et al.  Word Association Norms, Mutual Information, and Lexicography , 1989, ACL.

[12]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[13]  Peter D. Turney.  Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL , 2001, ECML.

[14]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[15]  Steffen Staab,et al.  Learning by googling , 2004, SKDD.

[16]  Stuart E. Middleton,et al.  Capturing knowledge of user preferences: ontologies in recommender systems , 2001, K-CAP '01.

[17]  David Snchez.  Domain Ontology Learning from the Web , 2008 .

[18]  Jun Ota,et al.  User-centered profile representation for recommendations across multiple content domains , 2011, Int. J. Knowl. Based Intell. Eng. Syst..

[19]  Shinichiro Takagi,et al.  Japanese Morphological Analyzer using Word Co-occurence -JTAG , 1998, COLING-ACL.

[20]  Gerard Salton,et al.  Term-Weighting Approaches in Automatic Text Retrieval , 1988, Inf. Process. Manag..

[21]  Stuart E. Middleton,et al.  Ontological user profiling in recommender systems , 2004, TOIS.

[22]  Sang-goo Lee,et al.  An Ontology-Based Product Recommender System for B2B Marketplaces , 2006, Int. J. Electron. Commer..