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

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

Combining Item Rating Similarity and Item Classification Similarity for Better Recommendation Quality

2012 ◽  
Vol 461 ◽  
pp. 289-292
Author(s):  
Kai Zhou
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Fundamental Problem ◽  
The Other ◽  
Recommendation Algorithm ◽  
Data Sparsity ◽  
Filtering Method ◽  
Product Recommendation ◽  
Sparsity Problem

Recommender systems are becoming increasingly popular, and collaborative filtering method is one of the most important technologies in recommender systems. The ability of recommender systems to make correct predictions is fundamentally determined by the quality and fittingness of the collaborative filtering that implements them. It is currently mainly used for business purposes such as product recommendation. Collaborative filtering has two types. One is user based collaborative filtering using the similarity between users to predict and the other is item based collaborative filtering using the similarity between items. Although both of them are successfully applied in wide regions, they suffer from a fundamental problem of data sparsity. This paper gives a personalized collaborative filtering recommendation algorithm combining the item rating similarity and the item classification similarity. This method can alleviate the data sparsity problem in the recommender systems

An Analysis of Group Recommendation Strategies

2010 ◽  
Vol 14 (6) ◽  
pp. 729-734 ◽  
Author(s):  
Shlomo Berkovsky ◽  
◽  
Jill Freyne
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Insufficient Data ◽  
Group Recommendation ◽  
Data Sparsity ◽  
Aggregated Data ◽  
Sparsity Problem ◽  
Group Recommendations

Collaborative filtering recommender systems often suffer from a data sparsity problem, where systems have insufficient data to generate accurate recommendations. To partially resolve this, the use of group aggregated data in the collaborative filtering recommendations process has been suggested. Although group recommendations are typically less accurate than personalized recommendations, they can be more accurate than generic ones, which are the natural fall back when personalized recommendations cannot be generated. This work presents a study that exploits a dataset of recipe ratings from families of users, in order to evaluate the accuracy of several group recommendation strategies and weighting models.

Alleviating the data sparsity problem of recommender systems by clustering nodes in bipartite networks

2020 ◽  
Vol 149 ◽  
pp. 113346
Author(s):  
Fuguo Zhang ◽  
Shumei Qi ◽  
Qihua Liu ◽  
Mingsong Mao ◽  
An Zeng
Keyword(s):  
Recommender Systems ◽  
Bipartite Networks ◽  
Data Sparsity ◽  
Sparsity Problem

scholarly journals Cross-Domain Recommendation: Challenges, Progress, and Prospects

2021 ◽  
Author(s):  
Feng Zhu ◽  
Yan Wang ◽  
Chaochao Chen ◽  
Jun Zhou ◽  
Longfei Li ◽  
...  
Keyword(s):  
Systematic Review ◽  
Recommender Systems ◽  
Research Progress ◽  
Data Sparsity ◽  
Comprehensive Review ◽  
Full View ◽  
Cross Domain ◽  
Single Target ◽  
Sparsity Problem ◽  
Dual Target

To address the long-standing data sparsity problem in recommender systems (RSs), cross-domain recommendation (CDR) has been proposed to leverage the relatively richer information from a richer domain to improve the recommendation performance in a sparser domain. Although CDR has been extensively studied in recent years, there is a lack of a systematic review of the existing CDR approaches. To fill this gap, in this paper, we provide a comprehensive review of existing CDR approaches, including challenges, research progress, and prospects. Specifically, we first summarize existing CDR approaches into four types, including single-target CDR, single-target multi-domain recommendation (MDR), dual-target CDR, and multi-target CDR. We then present the definitions and challenges of these CDR approaches. Next, we propose a full-view categorization and new taxonomies on these approaches and report their research progress in detail. In the end, we share several promising prospects in CDR.

scholarly journals Knowledge Transfer via Pre-training for Recommendation: A Review and Prospect

2021 ◽  
Vol 4 ◽  
Author(s):  
Zheni Zeng ◽  
Chaojun Xiao ◽  
Yuan Yao ◽  
Ruobing Xie ◽  
Zhiyuan Liu ◽  
...  
Keyword(s):  
Knowledge Transfer ◽  
Recommender Systems ◽  
Real World ◽  
Source Code ◽  
Cold Start ◽  
Future Research ◽  
Data Sparsity ◽  
Sparsity Problem

Recommender systems aim to provide item recommendations for users and are usually faced with data sparsity problems (e.g., cold start) in real-world scenarios. Recently pre-trained models have shown their effectiveness in knowledge transfer between domains and tasks, which can potentially alleviate the data sparsity problem in recommender systems. In this survey, we first provide a review of recommender systems with pre-training. In addition, we show the benefits of pre-training to recommender systems through experiments. Finally, we discuss several promising directions for future research of recommender systems with pre-training. The source code of our experiments will be available to facilitate future research.

scholarly journals Tensor Decomposition for Spatial—Temporal Traffic Flow Prediction with Sparse Data

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6046
Author(s):  
Funing Yang ◽  
Guoliang Liu ◽  
Liping Huang ◽  
Cheng Siong Chin
Keyword(s):  
Traffic Flow ◽  
Prediction Method ◽  
Sparse Data ◽  
Time Of Day ◽  
Traffic Prediction ◽  
Traffic Surveillance ◽  
Data Sparsity ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Sparsity Problem

Urban transport traffic surveillance is of great importance for public traffic control and personal travel path planning. Effective and efficient traffic flow prediction is helpful to optimize these real applications. The main challenge of traffic flow prediction is the data sparsity problem, meaning that traffic flow on some roads or of certain periods cannot be monitored. This paper presents a transport traffic prediction method that leverages the spatial and temporal correlation of transportation traffic to tackle this problem. We first propose to model the traffic flow using a fourth-order tensor, which incorporates the location, the time of day, the day of the week, and the week of the month. Based on the constructed traffic flow tensor, we either propose a model to estimate the correlation in each dimension of the tensor. Furthermore, we utilize the gradient descent strategy to design a traffic flow prediction algorithm that is capable of tackling the data sparsity problem from the spatial and temporal perspectives of the traffic pattern. To validate the proposed traffic prediction method, case studies using real-work datasets are constructed, and the results demonstrate that the prediction accuracy of our proposed method outperforms the baselines. The accuracy decreases the least with the percentage of missing data increasing, including the situation of data being missing on neighboring roads in one or continuous multi-days. This certifies that the proposed prediction method can be utilized for sparse data-based transportation traffic surveillance.

scholarly journals A New Weighted-learning Approach for Exploiting Data Sparsity in Tag-based Item Recommendation Systems

2021 ◽  
Vol 14 (1) ◽  
pp. 387-399
Author(s):  
Noor Ifada ◽  
◽  
Richi Nayak ◽  
Keyword(s):  
Recommendation System ◽  
Recommendation Systems ◽  
Learning Approach ◽  
Tensor Model ◽  
Learning Approaches ◽  
Data Sparsity ◽  
Tensor Models ◽  
Sparsity Problem ◽  
Weighted Rank

The tag-based recommendation systems that are built based on tensor models commonly suffer from the data sparsity problem. In recent years, various weighted-learning approaches have been proposed to tackle such a problem. The approaches can be categorized by how a weighting scheme is used for exploiting the data sparsity – like employing it to construct a weighted tensor used for weighing the tensor model during the learning process. In this paper, we propose a new weighted-learning approach for exploiting data sparsity in tag-based item recommendation system. We introduce a technique to represent the users’ tag preferences for leveraging the weighted-learning approach. The key idea of the proposed technique comes from the fact that users use different choices of tags to annotate the same item while the same tag may be used to annotate various items in tag-based systems. This points out that users’ tag usage likeliness is different and therefore their tag preferences are also different. We then present three novel weighting schemes that are varied in manners by how the ordinal weighting values are used for labelling the users’ tag preferences. As a result, three weighted tensors are generated based on each scheme. To implement the proposed schemes for generating item recommendations, we develop a novel weighted-learning method called as WRank (Weighted Rank). Our experiments show that considering the users' tag preferences in the tensor-based weightinglearning approach can solve the data sparsity problem as well as improve the quality of recommendation.

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