An Adaptive Technique for Weighting Multiple Factors in Followee Recommendation Algorithms

ANTONELA TOMMASEL; DANIELA GODOY

Buenos Aires

Workshop; Intelligent Personalization (IP) --- Joint Workshop on Constraints and Preferences for Configuration and Recommendation (CPCR) and Intelligent Techniques for Web Personalization (ITWP); 2015

International Joint Conference on Artificial Intelligence (IJCAI)

The accurate suggestion of interesting friends arises as a crucial issue in recommendation systems. The selection of friends or followees attends to several reasons whose importance might differ according to the characteristics and preferences of each user. Furthermore, those preferences might also change over time. Consequently, understanding how friends or followees are selected emerges as a key design factor of strategies for personalised recommendations. This work argues that the criteria for recommending followees needs to be adapted and combined according to each user´s behaviour, preferences, and characteristics. A technique is proposed for adapting such criteria to the characteristics of the previously selected followees. Moreover, the criteria can evolve over time to adapt to changes in user behaviour, and not only considers the similarity but also the novelty or diversity of the potential followees. Experimental evaluation showed that the proposed technique improved precision results regarding static weighting strategies. Furthermore, results highlighted the importance of adapting to the changes of user preferences over time.