952 research outputs found
Experience-based Personalized Diversification of Recommendations
Accuracy of the recommendations has long been regarded as the primary quality aspect of Recommender Systems (RS), but there's an increasing cognizance that there are other factors such as diversity that users also value. Despite the increased interest of researchers to improve diversification of recommendations, we find that personalization of diversification has been overlooked. As the preference for diversity changes from person-to-person, we propose a personalized diversification technique which is capable of controlling the trade-off between accuracy and diversity, where personalization is achieved by diversifying the recommendation list with more novel items if the user has shown diverse preferences in the past, and diversifying the recommendation list with more relevant items if the user has shown homogeneous preferences in the past. Moreover, we also introduce a novel recommendation technique which uses the past preferences of a user and the ratings of experienced item category experts in recommendation generation process. As post-filtering approaches generate the final diversified recommendation list by selecting items from a list generated from some RS, we use the recommendation technique we propose in order to generate an initial recommendation list with both novel and relevant items to improve the personalized diversification process. Our experiments and evaluation provides evidence to illustrate the properties of proposed techniques and indicate the proposed approach has comparable results to state-of-art techniques. Moreover, unlike other techniques, our approach can promote both novel and relevant items and also make the diversification process personalized
DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation
Graph Neural Network (GNN) based recommender systems have been attracting
more and more attention in recent years due to their excellent performance in
accuracy. Representing user-item interactions as a bipartite graph, a GNN model
generates user and item representations by aggregating embeddings of their
neighbors. However, such an aggregation procedure often accumulates information
purely based on the graph structure, overlooking the redundancy of the
aggregated neighbors and resulting in poor diversity of the recommended list.
In this paper, we propose diversifying GNN-based recommender systems by
directly improving the embedding generation procedure. Particularly, we utilize
the following three modules: submodular neighbor selection to find a subset of
diverse neighbors to aggregate for each GNN node, layer attention to assign
attention weights for each layer, and loss reweighting to focus on the learning
of items belonging to long-tail categories. Blending the three modules into
GNN, we present DGRec(Diversified GNN-based Recommender System) for diversified
recommendation. Experiments on real-world datasets demonstrate that the
proposed method can achieve the best diversity while keeping the accuracy
comparable to state-of-the-art GNN-based recommender systems.Comment: 9 pages, WSDM 202
Who are Like-minded: Mining User Interest Similarity in Online Social Networks
In this paper, we mine and learn to predict how similar a pair of users'
interests towards videos are, based on demographic (age, gender and location)
and social (friendship, interaction and group membership) information of these
users. We use the video access patterns of active users as ground truth (a form
of benchmark). We adopt tag-based user profiling to establish this ground
truth, and justify why it is used instead of video-based methods, or many
latent topic models such as LDA and Collaborative Filtering approaches. We then
show the effectiveness of the different demographic and social features, and
their combinations and derivatives, in predicting user interest similarity,
based on different machine-learning methods for combining multiple features. We
propose a hybrid tree-encoded linear model for combining the features, and show
that it out-performs other linear and treebased models. Our methods can be used
to predict user interest similarity when the ground-truth is not available,
e.g. for new users, or inactive users whose interests may have changed from old
access data, and is useful for video recommendation. Our study is based on a
rich dataset from Tencent, a popular service provider of social networks, video
services, and various other services in China
Probabilistic Neighborhood Selection in Collaborative Filtering Systems
This paper presents a novel probabilistic method for recommending items in the neighborhood-based collaborative filtering framework. For the probabilistic neighborhood selection phase, we use an efficient method for weighted sampling of k neighbors without replacement that also takes into consideration the similarity levels between the target user and the candidate neighbors. We conduct an empirical study showing that the proposed method alleviates the over-specialization and concentration biases in common recommender systems by generating recommendation lists that are very different from the classical collaborative filtering approach and also increasing the aggregate diversity and mobility of recommendations. We also demonstrate that the proposed method outperforms both the previously proposed user based k-nearest neighbors and k-furthest neighbors collaborative filtering approaches in terms of item prediction accuracy and utility based ranking measures across various experimental settings. This accuracy performance improvement is in accordance with ensemble learning theory.NYU Stern School of Busines
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