•Novel group hybrid method combining collaborative and content-based recommendation.•Proposed method improves the quality of recommended items ordering.•Proposed method increases the recommendation ...precision for very Top-N results.•Applicable for single user as well as group recommendation.
Nowadays, the increasing demand for group recommendations can be observed. In this paper we address the problem of recommendation performance for groups of users (group recommendation). We focus on the performance of very Top-N recommendations, which are important when recommending the long lasting items (only a few such items are consumed per session, e.g. movie). To improve existing group recommenders we propose a mixed hybrid recommender for groups combining content-based and collaborative strategies. The principle of proposed group recommender is to generate content and collaborative recommendations for each user, apply an aggregation strategy to solve the group conflict preferences for the content and collaborative sets separately, and finally reorder the collaborative candidates based on the content-based ones. It is based on an idea that candidates recommended by both recommendation strategies at the same time are presumably more appropriate for the group than the candidates recommended by individual strategies. The evaluation is performed by several experiments in the multimedia domain (as typical representative for group recommendations). Both, online and offline experiments were performed in order to compare real users’ satisfaction to the standard group recommenders and also, to compare performance of proposed approach to the state-of-the-art recommenders based on the MovieLens dataset. Finally, we experimented with the proposed hybrid recommender to generate the recommendation for a group of size one (i.e. single user recommendation). Obtained results, support our hypothesis that proposed mixed hybrid approach improves the precision of the recommendation for groups of users and for the single-user recommendation respectively on very Top-N recommended items.
The recommendation system needs to extract the historical data information of the relevant users on a large scale as the training set of the prediction model. The larger and more specific the amount ...of data provided by users is, the easier it is for the personal information to be inferred, which is easy to lead to the leakage of personal privacy, so that the user’s trust in the service provider is reduced and the relevant data are no longer provided for the system, resulting in the reduction of the system recommendation accuracy or even more difficult to complete the recommendation. Therefore, how to obtain user information for effective recommendation with high accuracy under the premise of protecting user privacy has become a research hotspot. This paper firstly summarizes the privacy-preserving technology, including differential privacy technology, homomorphic encryption technology, federated learning and secure multi-party computing technology, and compares these commonly used privacy-preserving tech-nolo
Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks. In ...recent years, we have witnessed significant progress in developing neural recommender models, which generalize and surpass traditional recommender models owing to the strong representation power of neural networks. In this survey paper, we conduct a systematic review on neural recommender models from the perspective of recommendation modeling with the accuracy goal, aiming to summarize this field to facilitate researchers and practitioners working on recommender systems. Specifically, based on the data usage during recommendation modeling, we divide the work into collaborative filtering and information-rich recommendation: 1) collaborative filtering , which leverages the key source of user-item interaction data; 2) content enriched recommendation , which additionally utilizes the side information associated with users and items, like user profile and item knowledge graph; and 3) temporal/sequential recommendation , which accounts for the contextual information associated with an interaction, such as time, location, and the past interactions. After reviewing representative work for each type, we finally discuss some promising directions in this field.
With the emergence of personality computing as a new research field related to artificial intelligence and personality psychology, we have witnessed an unprecedented proliferation of ...personality-aware recommendation systems. Unlike conventional recommendation systems, these new systems solve traditional problems such as the cold start and data sparsity problems. This survey aims to study and systematically classify personality-aware recommendation systems. To the best of our knowledge, this survey is the first that focuses on personality-aware recommendation systems. We explore the different design choices of personality-aware recommendation systems, by comparing their personality modeling methods, as well as their recommendation techniques. Furthermore, we present the commonly used datasets and point out some of the challenges of personality-aware recommendation systems.
Recommendation system (RS) is a technology that provides accurate recommendation for users. In order to make the recommendation results more accurate and diverse, we proposed a rating-based ...many-objective hybrid recommendation model that can optimize the accuracy, recall, diversity, novelty and coverage of the recommendation simultaneously. Additionally, a new generation-based fitness evaluation strategy and a partition-based knowledge mining strategy are proposed to improve the many-objective evolutionary algorithms (MaOEAs) to enhance the performance of the recommendations generated by the model. Finally, comparing the proposed many-objective optimization recommendation algorithm with the existing standard MaOEAs, experimental results demonstrate that the proposed algorithm can provide recommendations with the more and novel items on the basis of accuracy and diversity for users.
Extraneous growth of scientific information over the Internet makes the searching task non-trivial and as a consequence researchers are facing difficulties in finding relevant papers from the ...millions of research papers in digital repositories. The research paper recommendation systems have been advocated to address this problem. The existing research paper recommendation systems lack in exploiting prominent information of papers, such as relevancy with the current time, novelty, scientific contribution, writing complexity of the papers, etc. Further, the existing models emphasize only on user’s preference rather than user’s intention that may change with time. Furthermore, the existing models do not consider a sound ranking strategy to unleash the personalization aspect and relevancy of papers. This work aims to address the existing limitations and proposes a systematic hidden attribute-based recommendation engine (SHARE). SHARE utilizes a feature engineering technique to unfold valuable insights of papers through multiple hidden features. These features are used as a context for users as well as multiple criteria for ranking papers. Additionally, SHARE predicts a user’s intention beyond the user’s preference to capture the dynamic notion of a user. Finally, a novel ranking strategy is proposed to retrieve personalized and the most important papers. SHARE is flexible for recommending both old and new users. In order to evaluate the effectiveness of SHARE both user studies and system evaluations were performed. Experimental results substantiate the efficacy of the proposed approach and are comparable to the existing systems.
Tensor factorization has been applied in recommender systems to discover latent factors between multidimensional data such as time, place, and social context. However, tensor-based recommender ...systems still encounter with several problems such as sparsity, cold-start, and so on. In this paper, we introduce the new model social tensor to propose a tensor-based recommendation with a social relationship to deal with the existing problems. In addition, an adaptive method is presented to adjust the range of the social network for an active user. To evaluate our method, we conducted several experiments in the movie domain. The results indicate the ability of our method to improve the recommendation performance, even in the case of a new user. Particularly, the proposed method conducts the regeneration and factorization of the tensor in real time. Furthermore, our approach recommends not only a single item, but also the multi-factors for the item such as social, temporal, and spatial contexts.
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