1,720,960 research outputs found
Deep Rating Elicitation for New Users in Collaborative Filtering
No full textRecent recommender systems started to use rating elicitation, which asks new users to rate a small seed itemset for inferring their preferences, to improve the quality of initial recommendations. The key challenge of the rating elicitation is to choose the seed items which can best infer the new users' preference. This paper proposes a novel end-to-end Deep learning framework for Rating Elicitation (DRE), that chooses all the seed items at a time with consideration of the non-linear interactions. To this end, it first defines categorical distributions to sample seed items from the entire itemset, then it trains both the categorical distributions and a neural reconstruction network to infer users' preferences on the remaining items from CF information of the sampled seed items. Through the end-to-end training, the categorical distributions are learned to select the most representative seed items while reflecting the complex non-linear interactions. Experimental results show that DRE outperforms the state-of-the-art approaches in the recommendation quality by accurately inferring the new users' preferences and its seed itemset better represents the latent space than the seed itemset obtained by the other methods. © 2020 ACM.1
Item-side ranking regularized distillation for recommender system
No full textRecent recommender system (RS) have adopted large and sophisticated model architecture to better understand the complex user-item relationships, and accordingly, the size of the recommender is continuously increasing. To reduce the high inference costs of the large recommender, knowledge distillation (KD), which is a model compression technique from a large pre-trained model (teacher) to a small model (student), has been actively studied for RS. The state-of-the-art method is based on the ranking distillation approach, which makes the student preserve the ranking orders among items predicted by the teacher. In this work, we propose a new regularization method designed to maximize the effect of the ranking distillation in RS. We first point out an important limitation and a room for improvement of the state-of-the-art ranking distillation method based on our in-depth analysis.Then, we introduce the item-side ranking regularization, which can effectively prevent the student with limited capacity from being overfitted and enables the student to more accurately learn the teacher’s prediction results. We validate the superiority of the proposed method by extensive experiments on real-world datasets.11Nsciescopu
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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