Abstract
The rapid development of social networks has led to an increased desire for group entertainment consumption, making the study of group recommender systems a hotspot. Existing group recommender systems focus too much on member preferences and ignore the impact of member activity level on recommendation results. To this end, a dynamic group recommendation algorithm based on the activity level of members is proposed. Firstly, the algorithm predicts the unknown preferences of members using a time-series-oriented rating prediction model. Secondly, considering the dynamic change of member activity level, the group profile is generated by designing a sliding time window to investigate the recent activity level of each member in the group at the recommended moment, and preference is aggregated based on the recent activity level of members. Finally, the group recommendations are generated based on the group profile. The experimental results show that the algorithm in this paper achieves a better recommendation result.
Highlights
Recommender systems solve the problem of information overload and help users to choose from the options in our day to day life [1]. e traditional recommender systems use mathematical tools to analyze users’ historical behavior records, and draw their preferences to recommend products and services to them
We proposed a Dynamic Group Recommendation Algorithm Based on Member Activity Level (DMA) to overcome this potential problem and improve the quality of recommendations
Since the existing group recommendation algorithms generate group profiles directly based on member profiles, these methods would make the recommender system focus too much on member profiles and ignore the bias effect of member activity level on recommendations. erefore, we firstly proposed the concept of activity level and designed a sliding time window
Summary
Recommender systems solve the problem of information overload and help users to choose from the options in our day to day life [1]. e traditional recommender systems use mathematical tools to analyze users’ historical behavior records, and draw their preferences to recommend products and services to them. Researches on group recommender systems mainly focus on preference aggregation strategies, i.e., how to aggregate preferences of members to alleviate the preference conflict problem in the group as much as possible, so that the recommendations can meet the needs of all/most members. To solve this problem, various aggregation models have been proposed by scholars, such as the average strategy [7], the least misery strategy [8], the most pleasure strategy [9], the most respected strategy [10], etc.
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