Towards Comprehensive Recommender Systems: Time-Aware Unified Recommendations Based on Listwise Ranking of Implicit Cross-Network Data

The abundance of information in web applications make recommendation essential for users as well as applications. Despite the effectiveness of existing recommender systems, we find two major limitations that reduce their overall performance: (1) inability to provide timely recommendations for both new and existing users by considering the dynamic nature of user preferences, and (2) not fully optimized for the ranking task when using implicit feedback. Therefore, we propose a novel deep learning based unified cross-network solution to mitigate cold-start and data sparsity issues and provide timely recommendations for new and existing users. Furthermore, we consider the ranking problem under implicit feedback as a classification task, and propose a generic personalized listwise optimization criterion for implicit data to effectively rank a list of items. We illustrate our cross-network model using Twitter auxiliary information for recommendations on YouTube target network. Extensive comparisons against multiple time aware and cross-network baselines show that the proposed solution is superior in terms of accuracy, novelty and diversity. Furthermore, experiments conducted on the popular MovieLens dataset suggest that the proposed listwise ranking method outperforms existing state-of-the-art ranking techniques.

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Towards Cognitive Recommender Systems

Algorithms ◽

10.3390/a13080176 ◽

2020 ◽

Vol 13 (8) ◽

pp. 176

Author(s):

Amin Beheshti ◽

Shahpar Yakhchi ◽

Salman Mousaeirad ◽

Seyed Mohssen Ghafari ◽

Srinivasa Reddy Goluguri ◽

...

Keyword(s):

Recommender Systems ◽

State Of The Art ◽

User Preferences ◽

Data Driven ◽

New Approach ◽

Domain Experts ◽

Adaptive Mechanisms ◽

New Type ◽

Cognitive Activities ◽

Time Aware

Intelligence is the ability to learn from experience and use domain experts’ knowledge to adapt to new situations. In this context, an intelligent Recommender System should be able to learn from domain experts’ knowledge and experience, as it is vital to know the domain that the items will be recommended. Traditionally, Recommender Systems have been recognized as playlist generators for video/music services (e.g., Netflix and Spotify), e-commerce product recommenders (e.g., Amazon and eBay), or social content recommenders (e.g., Facebook and Twitter). However, Recommender Systems in modern enterprises are highly data-/knowledge-driven and may rely on users’ cognitive aspects such as personality, behavior, and attitude. In this paper, we survey and summarize previously published studies on Recommender Systems to help readers understand our method’s contributions to the field in this context. We discuss the current limitations of the state of the art approaches in Recommender Systems and the need for our new approach: A vision and a general framework for a new type of data-driven, knowledge-driven, and cognition-driven Recommender Systems, namely, Cognitive Recommender Systems. Cognitive Recommender Systems will be the new type of intelligent Recommender Systems that understand the user’s preferences, detect changes in user preferences over time, predict user’s unknown favorites, and explore adaptive mechanisms to enable intelligent actions within the compound and changing environments. We present a motivating scenario in banking and argue that existing Recommender Systems: (i) do not use domain experts’ knowledge to adapt to new situations; (ii) may not be able to predict the ratings or preferences a customer would give to a product (e.g., loan, deposit, or trust service); and (iii) do not support data capture and analytics around customers’ cognitive activities and use it to provide intelligent and time-aware recommendations.

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A Novel Time-Aware Hybrid Recommendation Scheme Combining User Feedback and Collaborative Filtering

Mobile Information Systems ◽

10.1155/2020/8896694 ◽

2020 ◽

Vol 2020 ◽

pp. 1-16

Author(s):

Hongzhi Li ◽

Dezhi Han

Keyword(s):

Collaborative Filtering ◽

Recommender Systems ◽

Clustering Algorithm ◽

Information Overload ◽

User Feedback ◽

User Preferences ◽

User Preference ◽

Logistic Regression Algorithm ◽

Hybrid Recommendation ◽

Time Aware

Nowadays, recommender systems are used widely in various fields to solve the problem of information overload. Collaborative filtering and content-based models are representative solutions in recommender systems; however, the content-based model has some shortcomings, such as single kind of recommendation results and lack of effective perception of user preferences, while for the collaborative filtering model, there is a cold start problem, and such a model is greatly affected by its adopted clustering algorithm. To address these issues, a hybrid recommendation scheme is proposed in this paper, which is based on both collaborative filtering and content-based. In this scheme, we propose the concept of time impact factor, and a time-aware user preference model is built based on it. Also, user feedback on recommendation items is utilized to improve the accuracy of our proposed recommendation model. Finally, the proposed hybrid model combines the results of content recommendation and collaborative filtering based on the logistic regression algorithm.

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Deep Matrix Factorization Models for Recommender Systems

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2017/447 ◽

2017 ◽

Cited By ~ 100

Author(s):

Hong-Jian Xue ◽

Xinyu Dai ◽

Jianbing Zhang ◽

Shujian Huang ◽

Jiajun Chen

Keyword(s):

Recommender Systems ◽

Loss Function ◽

Matrix Factorization ◽

Network Architecture ◽

Deep Structure ◽

Dimensional Space ◽

Information Matrix ◽

Auxiliary Information ◽

Personalized Recommendation ◽

Implicit Feedback

Recommender systems usually make personalized recommendation with user-item interaction ratings, implicit feedback and auxiliary information. Matrix factorization is the basic idea to predict a personalized ranking over a set of items for an individual user with the similarities among users and items. In this paper, we propose a novel matrix factorization model with neural network architecture. Firstly, we construct a user-item matrix with explicit ratings and non-preference implicit feedback. With this matrix as the input, we present a deep structure learning architecture to learn a common low dimensional space for the representations of users and items. Secondly, we design a new loss function based on binary cross entropy, in which we consider both explicit ratings and implicit feedback for a better optimization. The experimental results show the effectiveness of both our proposed model and the loss function. On several benchmark datasets, our model outperformed other state-of-the-art methods. We also conduct extensive experiments to evaluate the performance within different experimental settings.

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Modelling trust networks using resistive circuits for trust-aware recommender systems

Journal of Information Science ◽

10.1177/0165551516628733 ◽

2016 ◽

Vol 43 (1) ◽

pp. 135-144 ◽

Cited By ~ 4

Author(s):

Mehdi Hosseinzadeh Aghdam ◽

Morteza Analoui ◽

Peyman Kabiri

Keyword(s):

Collaborative Filtering ◽

Recommender Systems ◽

Recommender System ◽

Cold Start ◽

User Preferences ◽

Item Selection ◽

Trust Networks ◽

Trust Inference ◽

Trust Information

Recommender systems have been widely used for predicting unknown ratings. Collaborative filtering as a recommendation technique uses known ratings for predicting user preferences in the item selection. However, current collaborative filtering methods cannot distinguish malicious users from unknown users. Also, they have serious drawbacks in generating ratings for cold-start users. Trust networks among recommender systems have been proved beneficial to improve the quality and number of predictions. This paper proposes an improved trust-aware recommender system that uses resistive circuits for trust inference. This method uses trust information to produce personalized recommendations. The result of evaluating the proposed method on Epinions dataset shows that this method can significantly improve the accuracy of recommender systems while not reducing the coverage of recommender systems.

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Alleviating data sparsity problem in time-aware recommender systems using a reliable rating profile enrichment approach

Expert Systems with Applications ◽

10.1016/j.eswa.2021.115849 ◽

2022 ◽

Vol 187 ◽

pp. 115849

Author(s):

Sajad Ahmadian ◽

Nima Joorabloo ◽

Mahdi Jalili ◽

Milad Ahmadian

Keyword(s):

Recommender Systems ◽

Data Sparsity ◽

Sparsity Problem ◽

Time Aware

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A Vertical Handoff Decision Algorithm Based on QoS Evaluation

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.989-994.1464 ◽

2014 ◽

Vol 989-994 ◽

pp. 1464-1468

Author(s):

Yang Tao ◽

Kun Zhou

Keyword(s):

User Preferences ◽

Vertical Handoff ◽

Heterogeneous Wireless Network ◽

Analytic Hierarchy ◽

Full Account ◽

Decision Algorithm ◽

Target Network ◽

Simulation Results ◽

Qos Evaluation ◽

Hierarchy Process

In the next generation of heterogeneous wireless network environment, to meet the network requirements of diverse services ,we propose a vertical handoff decision algorithm based on QoS evaluation that refine the handover unit to services. The proposed algorithm consider the needs of the services、 network conditions、 user preferences and other factors, and makes Analytic Hierarchy Process (AHP) and cost function combine to choose the target network that is best meet the requirements of services . Comparing with the vertical handoff decision based on RSS, simulation results show that the proposed method can take full account of the different QoS requirements of various services types to choose the appropriate network, and would not cause performance degradation.

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Time-aware cloud manufacturing service selection using unknown QoS prediction and uncertain user preferences

Concurrent Engineering ◽

10.1177/1063293x211019503 ◽

2021 ◽

pp. 1063293X2110195

Author(s):

Ying Yu ◽

Shan Li ◽

Jing Ma

Keyword(s):

Time Series Prediction ◽

Service Selection ◽

Cloud Manufacturing ◽

Service Evaluation ◽

User Preferences ◽

User Preference ◽

Selection Methods ◽

Qos Prediction ◽

Qos Performance ◽

Time Aware

Selecting the most efficient from several functionally equivalent services remains an ongoing challenge. Most manufacturing service selection methods regard static quality of service (QoS) as a major competitiveness factor. However, adaptations are difficult to achieve when variable network environment has significant impact on QoS performance stabilization in complex task processes. Therefore, dynamic temporal QoS values rather than fixed values are gaining ground for service evaluation. User preferences play an important role when service demanders select personalized services, and this aspect has been poorly investigated for temporal QoS-aware cloud manufacturing (CMfg) service selection methods. Furthermore, it is impractical to acquire all temporal QoS values, which affects evaluation validity. Therefore, this paper proposes a time-aware CMfg service selection approach to address these issues. The proposed approach first develops an unknown-QoS prediction model by utilizing similarity features from temporal QoS values. The model considers QoS attributes and service candidates integrally, helping to predict multidimensional QoS values accurately and easily. Overall QoS is then evaluated using a proposed temporal QoS measuring algorithm which can self-adapt to user preferences. Specifically, we employ the temporal QoS conflict feature to overcome one-sided user preferences, which has been largely overlooked previously. Experimental results confirmed that the proposed approach outperformed classical time series prediction methods, and can also find better service by reducing user preference misjudgments.

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SetRank: A Setwise Bayesian Approach for Collaborative Ranking from Implicit Feedback

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i04.6077 ◽

2020 ◽

Vol 34 (04) ◽

pp. 6127-6136

Author(s):

Chao Wang ◽

Hengshu Zhu ◽

Chen Zhu ◽

Chuan Qin ◽

Hui Xiong

Keyword(s):

Bayesian Approach ◽

Posterior Probability ◽

State Of The Art ◽

User Preferences ◽

Implicit Feedback ◽

Pairwise Preference ◽

Entire List ◽

Collaborative Ranking ◽

Real World Datasets ◽

Made In

The recent development of online recommender systems has a focus on collaborative ranking from implicit feedback, such as user clicks and purchases. Different from explicit ratings, which reflect graded user preferences, the implicit feedback only generates positive and unobserved labels. While considerable efforts have been made in this direction, the well-known pairwise and listwise approaches have still been limited by various challenges. Specifically, for the pairwise approaches, the assumption of independent pairwise preference is not always held in practice. Also, the listwise approaches cannot efficiently accommodate “ties” due to the precondition of the entire list permutation. To this end, in this paper, we propose a novel setwise Bayesian approach for collaborative ranking, namely SetRank, to inherently accommodate the characteristics of implicit feedback in recommender system. Specifically, SetRank aims at maximizing the posterior probability of novel setwise preference comparisons and can be implemented with matrix factorization and neural networks. Meanwhile, we also present the theoretical analysis of SetRank to show that the bound of excess risk can be proportional to √M/N, where M and N are the numbers of items and users, respectively. Finally, extensive experiments on four real-world datasets clearly validate the superiority of SetRank compared with various state-of-the-art baselines.

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