Time-aware Recommender Systems Research Articles

Natural noise management in collaborative recommender systems over time-related information

AbstractRecommender systems are currently a suitable alternative for providing easy and appropriate access to information for users in today’s digital information-overloaded world. However, an important drawback of these systems is the inconsistent behavior of users in providing item preferences. To address this issue, several natural noise management (NNM) approaches have been proposed, which positively influence recommendation accuracy. However, a major limitation of such previous works is the disregarding of the time-related information coupled to the rating data in RSs. Based on this motivation, this paper proposes two novel methods, named SeqNNM and SeqNNM-p for NNM focused on an incremental, time-aware recommender system scenario that has not yet been considered, by performing a classification-based NNM over specific preference sequences, driven by their associated timestamps. Such methods have been evaluated by simulating a real-time scenario and using metrics such as mean absolute error, root-mean-square error, precision, recall, NDCG, number of modified ratings, and running time. The obtained experimental results show that in the used settings, it is possible to achieve better recommendation accuracy with a low intrusion degree. Furthermore, the main innovation associated with the overall contribution is the screening of natural noise management approaches to be used on specific preferences subsets, and not over the whole dataset as discussed by previous authors. These proposed approaches allow the use of natural noise management in large datasets, in which it would be very difficult to correct the entire data.

STAR: A session-based time-aware recommender system

Session-Based Recommenders (SBRs) are designed to predict users’ next actions based on their previous interactions within a session, without access to historical information about users. Modern SBRs leverage deep neural networks to capture users’ current interests and map them to a latent space, enabling prediction of their next preference. While state-of-the-art SBR models achieve satisfactory results, they often overlook the temporal details of events within sessions, focusing instead on the sequence of events. To address this limitation, we propose the STAR framework, which incorporates session temporal information to enhance the performance of SBRs. By incorporating time intervals between events within sessions, we construct more informative representations for both items and sessions. Our mechanism revises session representation by embedding time intervals without using discretization. Empirical results on the Yoochoose and Diginetica datasets demonstrate that our proposed method outperforms state-of-the-art baseline models in Recall and MRR criteria. Our approach highlights the potential of session temporal information in enhancing the performance of SBRs by capturing the momentary interests of anonymous users and their mindset shifts during sessions.

Value-Wise ConvNet for Transformer Models: An Infinite Time-Aware Recommender System

Finding the most suitable individual to answer a question using brief content has important usages, including the community of question answering systems and online recommender frameworks. However, one must tackle challenges: Disregarding the indispensable noise in short text contents, authors usually answer the input query with mismatched words that can negatively influence the textual relevance. Moreover, many vocabularies imply various alterations. Finally, not every expert is eager to answer an input query given the time constraint, named the reluctance dilemma. To overcome the challenges, we devise a novel embedding approach that constructs context-aware vectors. We then extract the knowledge domains out of the online contextual content. While we track user textual-temporal behavioral patterns via an infinite continuous-time module, we recommend a set of experts pertinent to the given query and willingly provide the response during the expected time. Experimental results on two real-world datasets of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">StackOverflow</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Yahoo</i> show that our online time-sensitive value-wise transformer can achieve higher effectiveness and efficiency versus other trending rivals in online expert recommendation systems. In addition, we empirically experience that Fourier transformers can automatically infer multi-aspect base signals and overpass manual discrete-time models in obtaining time-specific user profiles.

Time-Aware Recommender Systems: A Comprehensive Survey and Quantitative Assessment of Literature

Time-Aware Recommender Systems: A Comprehensive Survey and Quantitative Assessment of Literature

Temporal Knowledge Graph Embedding for Effective Service Recommendation

Over the last decade, service selection and recommendation had been two strongly related service filtering steps. While service selection aims to filter the best available services according to QoS and contextual criteria, service recommendation refines the selection results by taking into account additional criteria, such as users feedbacks and ratings, similarities between users tastes, etc. However, the ever changing services environment, users tastes, as well as the perception and popularity of available services, rise a question regarding the appropriate means to capture and analyze such changes over time. Most service recommendation solutions are static and do not offer a multi-relational modeling of user-service interactions over time. Time is a contextual dimension that has, recently, received a lot of attention, leading to a new class of recommender systems, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">time-aware recommender systems</i> . In this work, we propose a service recommendation method that takes advantage of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">temporal knowledge graphs</i> . As a de facto standard to model multiple and complex interactions between heterogeneous entities, knowledge graphs will serve as a historical knowledge base for our TASR system. We, first, model the user-service interactions over time, by constructing a temporal service knowledge graph (TSKG) that will be later enriched through a completion step. Second, to explore the TSKG and extract top-rated services, we use Convolutional Neural Networks (CNN) to embed the TSKG into a low-dimensional vector space, facilitating then its mining. Experimental studies have proven the effectiveness and accuracy of our approach, compared to traditional TASR methods and time-unaware KG-based recommendation.

RecMem: Time Aware Recommender Systems Based on Memetic Evolutionary Clustering Algorithm.

Nowadays, the recommendation is an important task in the decision-making process about the selection of items especially when item space is large, diverse, and constantly updating. As a challenge in the recent systems, the preference and interest of users change over time, and existing recommender systems do not evolve optimal clustering with sufficient accuracy over time. Moreover, the behavior history of the users is determined by their neighbours. The purpose of the time parameter for this system is to extend the time-based priority. This paper has been carried out a time-aware recommender systems based on memetic evolutionary clustering algorithm called RecMem for recommendations. In this system, clusters that evolve over time using the memetic evolutionary algorithm and extract the best clusters at every timestamp, and improve the memetic algorithm using the chaos criterion. The system provides appropriate suggestions to the user based on optimum clustering. The system uses optimal evolutionary clustering using item attributes for the cold-start item problem and demographic information for the cold start user problem. The results show that the proposed method has an accuracy of approximately 0.95, which is more effective than existing systems.

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

Recommender systems use intelligent algorithms to learn a user’s preferences and provide them relevant suggestions. Lack of sufficient ratings – also known as data sparsity problem – often results in poor recommendation performance. The existing recommendation methods have mainly focused on designing recommenders with high accuracy without paying much attention to the reliability of the recommendations. On the other hand, the users’ preferences may vary over time and considering the time factor in the design process is crucial, which has been largely ignored in most of the existing recommenders. To deal with these issues, a novel recommendation method is proposed in this paper which incorporates temporal reliability and confidence measures into the recommendation process. First, the effectiveness of the users’ rating is measured using a probabilistic approach and ineffective rating profiles are enriched by adding some implicit ratings to them. The quality of the predictions is evaluated using a temporal reliability measure taking into account the changes of users’ preferences over time. Then, the ratings with low reliability values are recalculated using a novel procedure, which updates the target user’s neighborhood by removing ineffective users. This leads to a temporal confidence measure that is used to update the neighborhood to provide more reliable and accurate recommendations. The superiority of the proposed method over state-of-the-art recommendation methods is shown by conducting extensive experiments on three benchmark datasets.

A Survey on Stream-Based Recommender Systems

Recommender Systems (RS) have proven to be effective tools to help users overcome information overload, and significant advances have been made in the field over the past two decades. Although addressing the recommendation problem required first a formulation that could be easily studied and evaluated, there currently exists a gap between research contributions and industrial applications where RS are actually deployed. In particular, most RS are meant to function in batch: they rely on a large static dataset and build a recommendation model that is only periodically updated. This functioning introduces several limitations in various settings, leading to considering more realistic settings where RS learn from continuous streams of interactions. Such RS are framed as Stream-Based Recommender Systems (SBRS). In this article, we review SBRS, underline their relation with time-aware RS and online adaptive learning, and present and categorize existing work that tackle the corresponding problem and its multiple facets. We discuss the methodologies used to evaluate SBRS and the adapted datasets that can be used, and finally we outline open challenges in the area.

A time-aware service recommendation based on implicit trust relationships and enhanced user similarities

In recent years, the ever-increasing number of cloud services has led to the service targeting issue. It becomes challenging to provide users with services that fit their needs. Recommender systems address the service targeting problem by helping users to easily retrieve services matching their tastes. Collaborative filtering-based methods are spreadly used recommendation approaches since numerous methods are based on them. However, most of them assume the stationarity of user profiles over time, which is an unrealistic consideration. Indeed, in practice, users’ tastes are dynamic and therefore show a significant variability over time. Time-aware recommender systems effectively integrate the variability of users’ needs to improve the recommendation quality. However, the recommendation credibility is another crucial requirement that is not met by those time-aware recommender systems that moreover badly deal with the data sparsity issue. To address the service targeting problem while meeting requirements on the recommendation reliability and the users’ tastes variability, we propose in this paper a realistic temporal service recommendation approach based on implicit trust relationships inference. Our method integrates the time feature into the recommendation process in order to consider the instantaneity of users’ needs. Our proposal is based on a trusted network that infers implicit trust relationships to ensure the recommendation credibility even in case of data sparsity. Experiments are conducted on real-world service invocations datasets. Our proposal is then compared to existing methods and displays valuable performances in terms of mean absolute error, root mean square error, and normalized discounted cumulative.

TIME-AWARE RECOMMENDER SYSTEM FOR E-COMMERCE APPLICATIONS

10Mar 2020 TIME-AWARE RECOMMENDER SYSTEM FOR E-COMMERCE APPLICATIONS Ayat Yehia Hassan , Etimad Fadel and Nadine Akkari Master Student, Department of Computer Science, King Abdul-Aziz University, Jeddah, Saudi Arabia. Associate Professor, Department of Computer Science, King Abdul-Aziz University, Jeddah, Saudi Arabia. Assistant Professor, Department of Computer Science, Lebanese University, Lebanon.

Recommendation over time: a probabilistic model of time-aware recommender systems

In time-aware recommender systems, we have to consider the dynamic aspect of recommendation that is fond of new coming data. Usually, the recent data is more closely related to current recommendation tasks and the early data are useful to indicate overall measurements of the preferences. We propose a probabilistic model that uses the early data to generate the prior distribution and the recent data to capture the change of the states of both users and items in collaborative filtering systems. Our model is dynamic in the sense that it updates every time receiving new data. The time cost of every updating has a constant limit, which is suitable to deal with large scale data for online recommendation. Experiments on real datasets show the improvement performance of our model over the existing time-aware recommender systems.

Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols

Exploiting temporal context has been proved to be an effective approach to improve recommendation performance, as shown, e.g. in the Netflix Prize competition. Time-aware recommender systems (TARS) are indeed receiving increasing attention. A wide range of approaches dealing with the time dimension in user modeling and recommendation strategies have been proposed. In the literature, however, reported results and conclusions about how to incorporate and exploit time information within the recommendation processes seem to be contradictory in some cases. Aiming to clarify and address existing discrepancies, in this paper we present a comprehensive survey and analysis of the state of the art on TARS. The analysis show that meaningful divergences appear in the evaluation protocols used--metrics and methodologies. We identify a number of key conditions on offline evaluation of TARS, and based on these conditions, we provide a comprehensive classification of evaluation protocols for TARS. Moreover, we propose a methodological description framework aimed to make the evaluation process fair and reproducible. We also present an empirical study on the impact of different evaluation protocols on measuring relative performances of well-known TARS. The results obtained show that different uses of the above evaluation conditions yield to remarkably distinct performance and relative ranking values of the recommendation approaches. They reveal the need of clearly stating the evaluation conditions used to ensure comparability and reproducibility of reported results. From our analysis and experiments, we finally conclude with methodological issues a robust evaluation of TARS should take into consideration. Furthermore we provide a number of general guidelines to select proper conditions for evaluating particular TARS.