Research of User Profile Model in Personalized Search

For personalized service, existing user interest model primarily through the select weights Highest N keywords to represent the user interest model based on space vector method. The method of establishing the model is tend to content-based analysis methods and there is a serious "cold start" problem, cannot meet the demand for personalized services. Therefore, this paper add collaborative filtering factor in the process of establishing user interest model, and verified by experiment, after adding personalization features which make the service more obvious. In a certain extent, solve the new user's "cold start" problem.

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The Design and Research of User Interest Model in Personalized Search Engine

2009 Asia-Pacific Conference on Information Processing ◽

10.1109/apcip.2009.294 ◽

2009 ◽

Author(s):

Liu Dong-Fei ◽

Duan Jian-Guo

Keyword(s):

Search Engine ◽

User Interest ◽

Personalized Search ◽

Interest Model ◽

User Interest Model

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User Profile Feature-Based Approach to Address the Cold Start Problem in Collaborative Filtering for Personalized Movie Recommendation

2018 Thirteenth International Conference on Digital Information Management (ICDIM) ◽

10.1109/icdim.2018.8847002 ◽

2018 ◽

Cited By ~ 1

Author(s):

Lasitha Uyangoda ◽

Supunmali Ahangama ◽

Tharindu Ranasinghe

Keyword(s):

Collaborative Filtering ◽

User Profile ◽

Cold Start ◽

Feature Based ◽

Movie Recommendation ◽

Cold Start Problem

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Enhancing Traditional Recommender Systems via Social Communities

Vietnam Journal of Computer Science ◽

10.1142/s2196888819500040 ◽

2019 ◽

Vol 06 (01) ◽

pp. 3-16

Author(s):

Leschek Homann ◽

Denis Mayr Lima Martins ◽

Gottfried Vossen ◽

Karsten Kraume

Keyword(s):

Collaborative Filtering ◽

Recommender Systems ◽

Social Information ◽

User Profile ◽

Cold Start ◽

Social Communities ◽

Social Network Data ◽

Business Applications ◽

Flexible Architecture ◽

Cold Start Problem

Collaborative Filtering (CF) has become the most popular approach for developing Recommender Systems in diverse business applications. Unfortunately, problems such as the cold-start problem (i.e., new users or items enter the system and for those no previous preference information is available) and the gray sheep problem (i.e., cases in which a user profile does not match any other profile in the user community) are widely recognized for hindering recommendation effectiveness of traditional CF methods. To alleviate such problems, substantial research has focused on enhancing CF with social information about users (e.g., social relationships and communities). However, despite the crescent interest in social-based approaches, researches and practitioners face the challenge of developing their own Recommender System architecture for appropriately combining social and collaborative filtering methods to improve recommendation results. In this paper, we address this issue by introducing a flexible architecture to support researchers and practitioners in the task of designing real-world Recommender Systems that exploit social network data. We focus on detailing our proposed architecture modules and their interplay, potential algorithms for extracting and combining relevant social information, and candidate technologies for handling diverse and massive data volumes. Additionally, we provide an empirical analysis demonstrating the effectiveness of the proposed architecture on alleviating the cold-start problem over a concrete experimental case.

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