Context-aware recommender systems in mobile environment: On the road of future research

Sassi, Imen Ben; Mellouli, Sehl; Yahia, Sadok Ben

doi:10.1016/j.is.2017.09.001

Cited by 63 publications

(37 citation statements)

References 43 publications

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“…In summary, our qualitative results show that offline connection, mutual friends, commonalities in city, college, or Facebook group, and interesting posts are major contextual factors that influence decisions on friend requests, especially those from unknown requesters. These results are in line with previous studies showing that offline contact (Strater & Lipford, 2008), mutual friends (Chen et al, 2009), interesting content (Ben Sassi et al, 2017;Chen et al, 2009;Guy et al, 2011), and common location history (Chen et al, 2009) significantly increase friend request acceptance. We thus refine H5-H9 to:…”

Section: Resultssupporting

confidence: 91%

“…For example, users are more likely to accept known requestors (Chen, Geyer, Dugan, Muller, & Guy, 2009), or those with whom they have minimal offline interaction (Strater & Lipford, 2008). When unknown persons send friend requests, users rely on common friends, interesting content (for instance, photos, lists, interests, and so on) and similarity of location (Ben Sassi, Mellouli, & Ben Yahia, 2017;Chen et al, 2009;Guy, Ur, Ronen, Perer, & Jacovi, 2011), as well as the social clues (Wisniewski et al, 2012). In information disclosure decisions, users consider the types of relationships with and the closeness of the audience in determining their willingness to share and the detail of the information to be shared.…”

Section: Context In Privacy Decisionsmentioning

confidence: 99%

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Context and Privacy Concerns in Friend Request Decisions

Kobsa

2019

Asso for Info Science & Tech

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Friend request acceptance and information disclosure constitute 2 important privacy decisions for users to control the flow of their personal information in social network sites (SNSs). These decisions are greatly influenced by contextual characteristics of the request. However, the contextual influence may not be uniform among users with different levels of privacy concerns. In this study, we hypothesize that users with higher privacy concerns may consider contextual factors differently from those with lower privacy concerns. By conducting a scenario-based survey study and structural equation modeling, we verify the interaction effects between privacy concerns and contextual factors. We additionally find that users' perceived risk towards the requester mediates the effect of context and privacy concerns. These results extend our understanding about the cognitive process behind privacy decision making in SNSs. The interaction effects suggest strategies for SNS providers to predict user's friend request acceptance and to customize context-aware privacy decision support based on users' different privacy attitudes.

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Section: Resultssupporting

confidence: 91%

Section: Context In Privacy Decisionsmentioning

confidence: 99%

Context and Privacy Concerns in Friend Request Decisions

Kobsa

2019

Asso for Info Science & Tech

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“…In order to make up for or avoid the problem of data sparseness and cold start of collaborative filtering, some scholars have proposed to combine multiple recommendation methods to achieve more realistic recommendation results by using a variety of mechanisms and hybrid collaborative filtering (Hybrid Collaborative Filtering, H-CF). Ben et al [41] classified the hybrid collaborative methods into pre-fusion, medium-fusion, and post-fusion according to the stage and degree of fusion among methods in the contextual recommendation process. In order to address the problem of online news recommendation, Claypool et al proposed a voting mechanism to integrate the prediction results based on content recommendation with that based on collaborative filtering recommendation [42].…”

Section: Sim Pearson Ijmentioning

confidence: 99%

Mobile Personalized Recommendation Model based on Privacy Concerns and Context Analysis for the Sustainable Development of M-commerce

2020

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A mobile personalized recommendation service satisfies the needs of users and stimulates them to continue to adopt mobile commerce applications. Therefore, how to precisely provide mobile personalized recommendation service is very important for the sustainable development of mobile commerce. However, privacy concerns regarding mobile commerce affect users’ consumption intentions, and also reduce the quality of mobile personalized recommendation services. In order to address this issue and the existing recommendation method problem in the mobile personalized recommendation service, this paper introduces six dimensions of privacy concerns and the relevant contextual information to propose a novel mobile personalized recommendation service based on privacy concerns and context analysis. First, this paper puts forward an intensity measurement method to measure the factors that influence privacy concerns, and then realizes a user-based collaborative filtering recommendation integrated with the intensity of privacy concerns. Second, a context similarity algorithm based on a context ontology-tree is proposed, after which this study realizes a user-based collaborative filtering recommendation integrated with context similarity. Finally, the research produces a hybrid collaborative filtering recommendation integrated with privacy concerns and context information. After experimental verification, the results show that this model can effectively solve the problems of data sparseness and cold starts. More importantly, it can reduce the influence of users’ privacy concerns on the adoption of mobile personalized recommendation services, and promote the sustainable development of mobile commerce.

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“…A Recommender System refers to a system that is capable of predicting the future preference of a set of items for a user, and recommends the top items [1]. The growth of RecSys has been progressed from the traditional RecSyss based on missing rating findings using collaborative filtering [2], content-based filtering [3] and hybrid RecSyss [4], to context aware [5], cross-domain [6] RecSyss and their complexities in nature leads to Deep Learning based RecSys models [7]. Those systems provide a more personalized way of finding items of interest of each user within a huge collection of products.…”

Section: Introductionmentioning

confidence: 99%

Gated Recurrent Unit Architecture for Context-Aware Recommendations with improved Similarity Measures

Kala¹,

Nandhini²

2020

KSII TIIS

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Recommender Systems (RecSys) have a major role in e-commerce for recommending products, which they may like for every user and thus improve their business aspects. Although many types of RecSyss are there in the research field, the state of the art RecSys has focused on finding the user similarity based on sequence (e.g. purchase history, moviewatching history) analyzing and prediction techniques like Recurrent Neural Network in Deep learning. That is RecSys has considered as a sequence prediction problem. However, evaluation of similarities among the customers is challenging while considering temporal aspects, context and multi-component ratings of the item-records in the customer sequences. For addressing this issue, we are proposing a Deep Learning based model which learns customer similarity directly from the sequence to sequence similarity as well as item to item similarity by considering all features of the item, contexts, and rating components using Dynamic Temporal Warping(DTW) distance measure for dynamic temporal matching and 2D-GRU (Two Dimensional-Gated Recurrent Unit) architecture. This will overcome the limitation of non-linearity in the time dimension while measuring the similarity, and the find patterns more accurately and speedily from temporal and spatial contexts. Experiment on the real world movie data set LDOS-CoMoDa demonstrates the efficacy and promising utility of the proposed personalized RecSys architecture.

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Context-aware recommender systems in mobile environment: On the road of future research

Cited by 63 publications

References 43 publications

Context and Privacy Concerns in Friend Request Decisions

Context and Privacy Concerns in Friend Request Decisions

Mobile Personalized Recommendation Model based on Privacy Concerns and Context Analysis for the Sustainable Development of M-commerce

Gated Recurrent Unit Architecture for Context-Aware Recommendations with improved Similarity Measures

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