A method for collaborative recommendation in document retrieval systems

Authors:
Bernadetta Mianowska;Ngoc Thanh Nguyen
Affiliations:
Institute of Informatics, Wroclaw University of Technology, Wroclaw, Poland;Institute of Informatics, Wroclaw University of Technology, Wroclaw, Poland
Venue:
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
Year:
2013

Citing 7
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International Journal of Human-Computer Studies - Special issue: 1969-1999, the 30th anniversary
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Information Sciences: an International Journal
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Information Sciences: an International Journal
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International Journal of Approximate Reasoning
Using knowledge integration techniques for user profile adaptation method in document retrieval systems

Transactions on computational collective intelligence V
A novel approach to hybrid recommendation systems based on association rules mining for content recommendation in asynchronous discussion groups

Information Sciences: an International Journal
Tuning user profiles based on analyzing dynamic preference in document retrieval systems

Multimedia Tools and Applications

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Abstract

The most common problem in the context of recommendation systems is "cold start" problem which occurs when new product is recommended or a new user becomes to the system. A great part of systems do not personalize a user until they gather sufficient information. In this paper a novel method for recommending a profile for a new user based only on knowledge about a few demographic data is proposed. The method merges a content-based approach with collaborative recommendation. The main objective was to show that based on knowledge about other similar users, the system can classify a new user based on subset of demographic data and recommend him a non-empty profile. Using the proposed profile, the user will obtain personalized documents. A methodology of experimental evaluation was presented and simulations were performed. The preliminary experiments have shown that the most important demographic attributes are gender, age, favorite browser and level of education.