%0 Journal Article %1 Zhou2017 %A Zhou, Dong %A Wu, Xuan %A Zhao, Wenyu %A Lawless, Seamus %A Liu, Jianxun %D 2017 %I IEEE Computer Society %J IEEE Transactions on Knowledge and Data Engineering %K Personalization,information alert and formulation,user profiles qe queryexpansion retrieval,query search services uncovr %N 7 %P 1536-1548 %R 10.1109/TKDE.2017.2668419 %T Query Expansion with Enriched User Profiles for Personalized Search Utilizing Folksonomy Data %V 29 %X Query expansion has been widely adopted in Web search as a way of tackling the ambiguity of queries. Personalized search utilizing folksonomy data has demonstrated an extreme vocabulary mismatch problem that requires even more effective query expansion methods. Co-occurrence statistics, tag-tag relationships, and semantic matching approaches are among those favored by previous research. However, user profiles which only contain a user's past annotation information may not be enough to support the selection of expansion terms, especially for users with limited previous activity with the system. We propose a novel model to construct enriched user profiles with the help of an external corpus for personalized query expansion. Our model integrates the current state-of-the-art text representation learning framework, known as word embeddings, with topic models in two groups of pseudo-aligned documents. Based on user profiles, we build two novel query expansion techniques. These two techniques are based on topical weights-enhanced word embeddings, and the topical relevance between the query and the terms inside a user profile, respectively. The results of an in-depth experimental evaluation, performed on two real-world datasets using different external corpora, show that our approach outperforms traditional techniques, including existing non-personalized and personalized query expansion methods.

@article{Zhou2017, abstract = {Query expansion has been widely adopted in Web search as a way of tackling the ambiguity of queries. Personalized search utilizing folksonomy data has demonstrated an extreme vocabulary mismatch problem that requires even more effective query expansion methods. Co-occurrence statistics, tag-tag relationships, and semantic matching approaches are among those favored by previous research. However, user profiles which only contain a user's past annotation information may not be enough to support the selection of expansion terms, especially for users with limited previous activity with the system. We propose a novel model to construct enriched user profiles with the help of an external corpus for personalized query expansion. Our model integrates the current state-of-the-art text representation learning framework, known as word embeddings, with topic models in two groups of pseudo-aligned documents. Based on user profiles, we build two novel query expansion techniques. These two techniques are based on topical weights-enhanced word embeddings, and the topical relevance between the query and the terms inside a user profile, respectively. The results of an in-depth experimental evaluation, performed on two real-world datasets using different external corpora, show that our approach outperforms traditional techniques, including existing non-personalized and personalized query expansion methods.}, added-at = {2021-10-15T08:50:32.000+0200}, author = {Zhou, Dong and Wu, Xuan and Zhao, Wenyu and Lawless, Seamus and Liu, Jianxun}, biburl = {https://www.bibsonomy.org/bibtex/24bae09d353cebbc10504db2b5c2685da/simonha94}, doi = {10.1109/TKDE.2017.2668419}, interhash = {1b867b9bbcb8965edf7db3a3a3319dd3}, intrahash = {4bae09d353cebbc10504db2b5c2685da}, journal = {IEEE Transactions on Knowledge and Data Engineering}, keywords = {Personalization,information alert and formulation,user profiles qe queryexpansion retrieval,query search services uncovr}, month = {7}, number = 7, pages = {1536-1548}, publisher = {IEEE Computer Society}, timestamp = {2021-10-15T08:52:26.000+0200}, title = {Query Expansion with Enriched User Profiles for Personalized Search Utilizing Folksonomy Data}, volume = 29, year = 2017 }