Web Service Recommendation Based on Watchlist via Temporal and Tag Preference Fusion

Zhang, Xiuwei; He, Ke; Wang, Jian; Wang, Chong; Tian, Gang; Liu, Jianxiao

doi:10.1109/icws.2014.49

Cited by 11 publications

(26 citation statements)

References 19 publications

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“…As shown in the work of Zhang et al, 9 the popularity of an item decreases over time. As shown in the work of Zhang et al, 9 the popularity of an item decreases over time.…”

Section: Contextual Top N Web Services Recommendationmentioning

confidence: 90%

“…8,9 For example, Zhang et al proposed a mechanism that transforms implicit feedbacks into numerical ratings to improve the accuracy of Web service recommendation by integrating temporal and tag information. Because implicit feedbacks are collected from the interactions between users and systems, they can highly represent the preferences of users.…”

Section: Implicit Feedback Techniquesmentioning

confidence: 99%

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Leveraging contextual information for cold‐start Web service recommendation

Tian

Wang

et al. 2019

Concurrency and Computation

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Web service recommendation becomes an increasingly important issue when more and more services are published on the Internet. Many Web service recommendation methods have been proposed in recent years, most of which adopted collaborative filtering (CF) techniques.In general, these approaches have two limitations. Firstly, they rarely leverage user ratings since this kind of explicit feedback is difficult to collect for Web services. Secondly, the new user cold-start problem is an inherent limitation of CF because the new users have not yet cast sufficient numbers of votes. In this paper, pseudo ratings of services constructed based on plenty of user-service interactions, also known as a kind of implicit feedback, are provided to represent users' preferences on services. Based on these pseudo ratings, we present a novel Web service recommendation approach, which can alleviate the cold-start problem by integrating contextual information and an online learning model. Experiments conducted on a real world data set show that, compared with the method without contextual information, our proposed approach that handles the cold start problem by integrating contextual information can achieve better F-Measure performance (5.08 times increase on average). Moreover, the proposed online recommendation approach can dramatically decrease the time overhead while keeping the similar recommendation performance. KEYWORDS cold-start web service recommendation, implicit feedback, probability matrix factorization

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“…As shown in the work of Zhang et al, 9 the popularity of an item decreases over time. As shown in the work of Zhang et al, 9 the popularity of an item decreases over time.…”

Section: Contextual Top N Web Services Recommendationmentioning

confidence: 90%

Section: Implicit Feedback Techniquesmentioning

confidence: 99%

Leveraging contextual information for cold‐start Web service recommendation

Tian

Wang

et al. 2019

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

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“…Existing approaches such as content‐based, memory‐based, and trust‐based are generally continuous. Shifting of service bias and shifting of user preferences are also the most used time effects in categorical TARS.…”

Section: Time Information In Service Recommendationmentioning

confidence: 99%

Web service recommendation based on time‐aware users clustering and multi‐valued QoS prediction

Fayala¹,

Mezni

2019

Concurrency and Computation

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Summary With the growing number of functionally similar services over the Internet, recommendation techniques become a natural choice to cope with the challenging task of optimal service selection, and to help consumers satisfy their needs and preferences. However, most existing models on service recommendation are static, while in the real world, the perception and popularity of Web services may continually change. Time is becoming an increasingly important factor in recommender systems since time effects influence users' preferences to a large extent. In order to help users with this problem, we propose a time‐aware Web service recommendation system. First, we use K‐means clustering method in order to exclude the less similar users, which share few common Web services with the active user at different times. Slope One algorithm is also adopted in order to deal with data sparsity problem by predicting the missing ratings over time. Then, a recommendation algorithm is presented in order to recommend the top‐rated Web services. Experiments proved the accuracy of our approach compared to five existing solutions.

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“…First, the temporal contexts [5][6][7][8] have been widely used in conventional CARS methods. The "user-service-time" triadic relations are represented in [5] to analyze latent features in recommendation by a three-dimensional tensor.…”

Section: Related Workmentioning

confidence: 99%

“…Preliminary benefits have been seen in recommending web services when taking contextual factors into account [2][3][4]. Specifically, temporal [5][6][7][8], spatial [4,9,11] and social [10] contexts are extracted separately for personalized web services recommendation.…”

Section: Introductionmentioning

confidence: 99%

Modeling Temporal Effectiveness for Context-Aware Web Services Recommendation

Fan

Zhang

et al. 2015

2015 IEEE International Conference on Web Services

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Context-Aware Recommender System (CARS) aims to not only recommend services similar to those already rated with the highest score, but also provide opportunities for exploring the important role of temporal, spatial and social contexts for personalized web services recommendation. A key step for temporal-based CARS methods is to explore the time decay process of past invocation records to make the Quality of Services (QoS) prediction. However, it is a nontrivial task to model the temporal effects on web services recommendation, due to the dynamic features of contextual information in view of temporal spatial correlations. For instance, in locationaware services recommendation, the user's geographical position would change very frequently as time goes on. In this paper, we propose a Context-Aware Services Recommendation based on Temporal Effectiveness (CASR-TE) method. Inspired by existing time decay approaches, we first present an enhanced temporal decay model combining the time decay function with traditional similarity measurement methods. Then, we model temporal spatial correlations as well as their impacts on the user preference expansion. Finally, we evaluate the CASR-TE method on WS-Dream dataset by evaluation matrices of both RMSE and MAE. Experimental results show that our approach outperforms several benchmark methods with a significant margin.

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Web Service Recommendation Based on Watchlist via Temporal and Tag Preference Fusion

Cited by 11 publications

References 19 publications

Leveraging contextual information for cold‐start Web service recommendation

Leveraging contextual information for cold‐start Web service recommendation

Web service recommendation based on time‐aware users clustering and multi‐valued QoS prediction

Modeling Temporal Effectiveness for Context-Aware Web Services Recommendation

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