An extensive study on the evolution of context-aware personalized travel recommender systems
یک مطالعه گسترده در مورد تکامل سیستمهای توصیه گر سفر شخصی آگاه از متن-2020
Ever since the beginning of civilization, travel for various causes exists as an essential part of human life so as travel recommendations, though the early form of recommendations were the accrued experiences shared by the community. Modern recommender systems evolved along with the growth of Information Technology and are contributing to all industry and service segments inclusive of travel and tourism. The journey started with generic recommender engines which gave way to personalized recommender systems and further advanced to contextualized personalization with advent of artificial intelligence. Current era is also witnessing a boom in social media usage and the social media big data is acting as a critical input for various analytics with no exception for recommender systems. This paper details about the study conducted on the evolution of travel recommender systems, their features and current set of limitations. We also discuss on the key algorithms being used for classification and recommendation processes and metrics that can be used to evaluate the performance of the algorithms and thereby the recommenders.
Keywords: Recommender system | Personalization | Context aware | Big data | Travel and tourism
Top-aware reinforcement learning based recommendation
توصیه مبتنی بر یادگیری تقویتی آگاه برتر -2020
Reinforcement learning (RL) techniques have recently been introduced to recommender systems. Most existing research works focus on designing policy and learning algorithms of the recommender agent but seldom care about the top-aware issue, i.e., the performance on the top positions is not satisfying, which is crucial for real applications. To address the drawback, we propose a Supervised deep Reinforcement learning Recommendation framework named as SRR. Within this framework, we utilize a supervised learning (SL) model to partially guide the learning of recommendation policy, where the supervision signal and RL signal are jointly employed and updated in a complementary fashion. We empirically find that suitable weak supervision helps to balance the immediate reward and the longterm reward, which nicely addresses the top-aware issue in RL based recommendation. Moreover, we perform a further investigation on how different supervision signals impact on recommendation policy. Extensive experiments are carried out on two real-world datasets under both the offline and simulated online evaluation settings, and the results demonstrate that the proposed methods indeed resolve the top-aware issue without much performance sacrifice in the long-run, compared with the state-of-theart methods.
Keywords: Recommendation | Top-aware | Reinforcement learning
State representation modeling for deep reinforcement learning based recommendation
مدل سازی نمایندگی حالت برای توصیه مبتنی بر یادگیری تقویتی عمیق-2020
Reinforcement learning techniques have recently been introduced to interactive recommender systems to capture the dynamic patterns of user behavior during the interaction with recommender systems and perform planning to optimize long-term performance. Most existing research work focuses on designing policy and learning algorithms of the recommender agent but seldom cares about the state representation of the environment, which is indeed essential for the recommendation decision making. In this paper, we first formulate the interactive recommender system problem with a deep reinforcement learning recommendation framework. Within this framework, we then carefully design four state representation schemes for learning the recommendation policy. Inspired by recent advances in feature interaction modeling in user response prediction, we discover that explicitly modeling user– item interactions in state representation can largely help the recommendation policy perform effective reinforcement learning. Extensive experiments on four real-world datasets are conducted under both the offline and simulated online evaluation settings. The experimental results demonstrate the proposed state representation schemes lead to better performance over the state-of-the-art methods.
Keywords: State representation modeling | Deep reinforcement learning | Recommendation
A social-semantic recommender system for advertisements
یک سیستم پیشنهادی اجتماعی معنایی برای تبلیغات-2020
Social applications foster the involvement of end users in Web content creation, as a result of which a new source of vast amounts of data about users and their likes and dislikes has become available. Having access to users’ contributions to social sites and gaining insights into the consumers’ needs is of the utmost importance for marketing decision making in general, and to advertisement recommendation in particular. By analyzing this information, advertisement recommendation systems can attain a better understanding of the users’ interests and preferences, thus allowing these solutions to provide more precise ad suggestions. However, in addition to the already complex challenges that hamper the performance of recommender systems (i.e., data sparsity, cold-start, diversity, accuracy and scalability), new issues that should be considered have also emerged from the need to deal with heterogeneous data gathered from disparate sources. The technologies surrounding Linked Data and the Semantic Web have proved effective for knowledge management and data integration. In this work, an ontology-based advertisement recommendation system that leverages the data produced by users in social networking sites is proposed, and this approach is substantiated by a shared ontology model with which to represent both users’ profiles and the content of advertisements. Both users and advertisement are represented by means of vectors generated using natural language processing techniques, which collect ontological entities from textual content. The ad recommender framework has been extensively validated in a simulated environment, obtaining an aggregated f-measure of 79.2% and a Mean Average Precision at 3 (MAP@3) of 85.6%.
Keywords:Knowledge-based systems | Recommender systems | Natural language processing | Advertising | Social network services
Multiobjective e-commerce recommendations based on hypergraph ranking
توصیه های تجارت الکترونیکی چندوجهی مبتنی بر رتبه بندی هایپرگراف-2019
Recommender systems are emerging in e-commerce as important promotion tools to assist customers to discover potentially interesting items. Currently, most of these are single- objective and search for items that ﬁt the overall preference of a particular user. In real applications, such as restaurant recommendations, however, users often have multiple ob- jectives such as group preferences and restaurant ambiance. This paper highlights the need for multi-objective recommendations and provides a solution using hypergraph ranking. A general User–Item–Attribute–Context data model is proposed to summarize different in- formation resources and high-order relationships for the construction of a multipartite hy- pergraph. This study develops an improved balanced hypergraph ranking method to rank different types of objects in hypergraph data. An overall framework is then proposed as a guideline for the implementation of multi-objective recommender systems. Empirical ex- periments are conducted with the dataset from a review site Yelp.com, and the outcomes demonstrate that the proposed model performs very well for multi-objective recommenda- tions. The experiments also demonstrate that this framework is still compatible for tradi- tional single-objective recommendations and can improve accuracy signiﬁcantly. In conclu- sion, the proposed multi-objective recommendation framework is able to handle complex and changing demands for e-commerce customers.
Keywords: Recommender systems | E-commerce | User personalization | Hypergraph
An efficient manifold regularized sparse non-negative matrix factorization model for large-scale recommender systems on GPUs
یک مدل فاکتور گیری ماتریس غیر منفی خلوت منظم شده چند ظرفیتی کارا برای سیستمهای توصیه گر در مقیاس بزرگ بر روی GPU-2019
Article history:Received 31 January 2018Revised 1 July 2018Accepted 25 July 2018Available online 27 July 2018Keywords:Collaborative ﬁltering recommender systemsData miningEuclidean distance and KL-divergence GPU parallelizationManifold regularizationNon-negative matrix factorizationNon-negative Matrix Factorization (NMF) plays an important role in many data mining ap- plications for low-rank representation and analysis. Due to the sparsity that is caused by missing information in many high-dimension scenes, e.g., social networks or recommender systems, NMF cannot mine a more accurate representation from the explicit information. Manifold learning can incorporate the intrinsic geometry of the data, which is combined with a neighborhood with implicit information. Thus, manifold-regularized NMF (MNMF) can realize a more compact representation for the sparse data. However, MNMF suffers from (a) the forming of large-scale Laplacian matrices, (b) frequent large-scale matrix ma- nipulation, and (c) the involved K-nearest neighbor points, which will result in the over- writing problem in parallelization. To address these issues, a single-thread-based MNMF model is proposed on two types of divergence, i.e., Euclidean distance and Kullback–Leibler (KL) divergence, which depends only on the involved feature-tuples’ multiplication and summation and can avoid large-scale matrix manipulation. Furthermore, this model can remove the dependence among the feature vectors with ﬁne-grain parallelization inher- ence. On that basis, a CUDA parallelization MNMF (CUMNMF) is presented on GPU com- puting. From the experimental results, CUMNMF achieves a 20X speedup compared with MNMF, as well as a lower time complexity and space requirement.© 2018 Published by Elsevier Inc.
Keywords: Collaborative filtering recommender systems | Data mining | Euclidean distance and KL-divergence | GPU parallelization | Manifold regularization | Non-negative matrix factorization
GPS: Factorized group preference-based similarity models for sparse sequential recommendation
GPS: مدلهای شباهت مبتنی بر اولویت گروهی فاکتور شده برای توصیه های پی در پی پراکنده-2019
One of the key tasks for recommender systems is the prediction of personalized sequential behavior. There are two primary means of modeling sequential patterns and long-term user preferences: Markov chains and matrix factorization, respectively. Together, they provide a unified approach to predicting user actions. In spite of their strengths in tackling dense data, however, these methods struggle with the sparsity issues often present in realworld datasets. In approaching this problem, we propose combining similarity-based methods (demonstrably helpful for sequentially unaware item recommendation) with Markov chains to offer individualized sequential recommendations. This approach, called GPS (a factorized group preference-based similarity model), further leverages the idea of group preference along with user preference to introduce a greater array of interactions between users—which in turn eases the problem of data sparsity and cold users and cuts down on the assumption of a strong independency within various factors. By applying our method to a range of large, real-world datasets, we demonstrate quantitatively that GPS outperforms several state-of-the-art methods, particularly in cases with sparse datasets. Regarding qualitative findings, GPS also grasps personalized interactions and can provide recommendations that are both on-target and meaningful.
Keywords: Recommender systems | Sequential recommendation | Similarity models | Group preference
Towards a more reliable privacy-preserving recommender system
به سمت یک سیستم توصیه گر برای حفظ حریم خصوصی قابل اطمینان تر-2019
This paper proposes a privacy-preserving distributed recommendation framework, Secure Distributed Collaborative Filtering (SDCF), to preserve the privacy of value, model and ex- istence altogether. That says, not only the ratings from the users to the items, but also the existence of the ratings as well as the learned recommendation model are kept pri- vate in our framework. Our solution relies on a distributed client-server architecture and a two-stage Randomized Response algorithm, along with an implementation on the pop- ular recommendation model, Matrix Factorization (MF). We further prove SDCF to meet the guarantee of Differential Privacy so that clients are allowed to specify arbitrary privacy levels. Experiments conducted on numerical rating prediction and one-class rating action prediction exhibit that SDCF does not sacriﬁce too much accuracy for privacy.© 2019 Elsevier Inc. All rights reserved.
Keywords: Privacy-preserving recommendation | Differential privacy | Secure distributed matrix factorization | Randomized response algorithms
A sub-one quasi-norm-based similarity measure for collaborative filtering in recommender systems
اندازه گیری شباهت sub-one مبتنی بر شبه هنجار برای فیلتر مشترک در سیستم های توصیه گر-2019
Collaborative filtering (CF) is one of the most successful approaches for an online store to make personalized recommendations through its recommender systems. A neighborhoodbased CF method makes recommendations to a target customer based on the similar preference of the target customer and those in the database. Similarity measuring between users directly contributes to an effective recommendation. In this paper, we propose a sub-one quasi-norm-based similarity measure for collaborative filtering in a recommender system. The proposed similarity measure shows its advantages over those commonly used similarity measures in the literature by making better use of rating values and deemphasizing the dissimilarity between users. Computational experiments using various real-life datasets clearly indicate the superiority of the proposed similarity measure, no matter in fully co-rated, sparsely co-rated or cold-start scenarios.
Keywords: Recommender system | Collaborative filtering | Neighborhood-based CF | Similarity measure | p quasi-norm
Modeling Side Information in Preference Relation based Restricted Boltzmann Machine for recommender systems
مدل سازی اطلاعات جانبی در دستگاه تنظیم بولتزمن محدود مبتنی بر رابطه برای سیستم های توصیه گر-2019
A majority of the collaborative filtering techniques exploit user-item rating in- formation to generate recommendations of unseen items for a user. However, a user’s preference also depends on some extra information like item features, user attributes and others, which is known as side information. Further, according to recent studies, us- ing preference relation as an alternative to absolute ratings often produces qual- ity recommendations. This study proposes a collaborative filtering technique using Pref- erence Relation based Restricted Boltzmann Machine for recommender system. The pro- posed method takes the preference relations of items as input and generates a ranking of items for any user. Using Conditional Restricted Boltzmann Machine, the side informa- tion of items along with preference relations are integrated into the model. Besides side information, the proposed method is also able to capture second order and higher order user-item interactions. Experimental verification of the proposed model is done using three datasets: MovieLens-1M, MovieLens-20M, and Book-Crossing, which are the most widely used datasets for testing recommender systems. Results obtained at different positions us- ing standard ranking measures like, NDCG and MAP, indicate that the performance of the proposed method is better compared to related state-of-the-art collaborative filtering mod- els for Top-N recommendation task.
Keywords: Recommender system | Collaborative Filtering | Side information | Restricted Boltzmann Machine | Preference relation