A Context-Aware Method for Top-k Recommendation in Smart TV

Liu, Peng; Ma, Jun; Wang, Yongjin; Ma, Lintao; Huang, Shanshan

doi:10.1007/978-3-319-45817-5_12

Cited by 3 publications

(6 citation statements)

References 12 publications

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“…The adjustment can be made either by sorting the list according to given context information as mentioned previously or by filtering out the irrelevant recommendations. Some studies recommend implementing post-filtering to reorder the recommendation list so that recommended information may be more suitable for the current circumstance [25].…”

Section: ) Context Integrationmentioning

confidence: 99%

“…The idea is to collect sets of videos watched by each user with their corresponding timestamps. Based on that, the strong associations share their watching list according to their relevance [25].…”

Section: A Social-based Recommender Systemmentioning

confidence: 99%

“…Collaborative filtering [25], [30], [32], [36], [42], [43], [52], [58], [59], [63], [71], [72], [94], [95], [97], [101], [107], [108], [110] [42], [43], [52], [72], [75], [82], [83], [91], [97], [101], [105] Content based filtering [65], [113] Graph [88] [44], [53], [62], [77],…”

Section: Sparsitymentioning

confidence: 99%

“…Table III shows the distribution of studies based on the different techniques that are used in recommendation approaches. Techniques References Bayesian probability [80], [90] Bipartite graphs embedding [84] Clustering [30], [60], [70], [91], [107] Decision tree [26], [38], [83] Deep neural networks [35], [56], [59], [65], [71], [72], [73], [76], [93], [98], [99], [100], [108] Distance-based method [88], [89] Fuzzy clustering [113] K Nearest Neighbor [24], [25], [44], [54], [75]…”

Section: Sparsitymentioning

confidence: 99%

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Context-Aware Recommender Systems for Social Networks: Review, Challenges and Opportunities

Suhaim

Berri

2021

IEEE Access

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Context-aware recommender systems dedicated to online social networks experienced noticeable growth in the last few years. This has led to more research being done in this area stimulated by the omnipresence of smartphones and the latest web technologies. These systems are able to detect specific user needs and adapt recommendations to actual user context. In this research, we present a comprehensive review of context-aware recommender systems developed for social networks. For this purpose, we used a systematic literature review methodology which clearly defined the scope, the objective, the timeframe, the methods, and the tools to undertake this research. Our focus is to investigate approaches and techniques used in the development of context-aware recommender systems for social networks and identify the research gaps, challenges, and opportunities in this field. In order to have a clear vision of the research potential in the field, we considered research articles published between 2015 and 2020 and used a research portal giving access to major scientific research databases. Primary research articles selected are reviewed and the recommendation process is analyzed to identify the approach, the techniques, and the context elements employed in the development of the recommendation systems. The paper presents the detail of the review study, provides a synthesis of the results, proposes an evaluation based on measurable evaluation tools developed in this study, and advocates future research and development pathways in this interesting field.

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Section: ) Context Integrationmentioning

confidence: 99%

Section: A Social-based Recommender Systemmentioning

confidence: 99%

Section: Sparsitymentioning

confidence: 99%

Section: Sparsitymentioning

confidence: 99%

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Context-Aware Recommender Systems for Social Networks: Review, Challenges and Opportunities

Suhaim

Berri

2021

IEEE Access

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“…Collaborative filtering techniques have issues, such as the cold-start problem, data sparsity [53], grey sheep problem, scalability problem, synonym problem [26]. To overcome the issues of the cold-start problem in smart TV scenario, the Top-N algorithm, which is a non-personalized algorithm, recommends the top-most rated items to a user [54].…”

Section: B Issues From Recommender Systems Perspectivesmentioning

confidence: 99%

Tailoring Recommendations to Groups of Viewers on Smart TV: A Real-Time Profile Generation Approach

Alam

Khusro

2020

IEEE Access

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The recommender systems predict and calculate user preferences for recommendations. However, such predictions and calculations are neither accurate nor viable in the context of smart TV due to the reasons that it is a lean-back, non-personalized device, and normally enjoyed in groups. Hence, group recommendations have utmost importance, specifically from the perspectives of watching smart TV. The existing group recommendation techniques predict the individual's preferences and then create a virtual group profile for recommendations. However, identifying and satisfying every group member is challenging. Numerous techniques have been proposed, such as face detection and recognition systems, but these systems lead to security and privacy issues. This paper proposes a smart TV-based recommender system that aims to identify the individuals and group members from their ''age,'' ''gender,'' and ''number'' information to overcome the biases that occur due to predictions and estimations of user's preferences. The study proposes a novel formula and age-gender matrix for generating anonymous, consolidated, and secure profiles, including group profiles on a smart TV. This study further proposes a novel method for finding a dominant character in a group by utilizing the user's ratings. Results show that the group decision has a significant impact on supplying social metadata, such as ratings, comments, etc., which in turn improve recommendation results. For materializing the proposed work, smart TV's processing, storage, and camera are utilized. The prototypical implementation has been tested and analyzed with improved recommendation results and viewer(s) satisfaction. INDEX TERMS Recommender systems, smart TV, group modeling, personalized content.

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Predicting product advertisement links using hybrid learning within social networks

2023

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A Context-Aware Method for Top-k Recommendation in Smart TV

Cited by 3 publications

References 12 publications

Context-Aware Recommender Systems for Social Networks: Review, Challenges and Opportunities

Context-Aware Recommender Systems for Social Networks: Review, Challenges and Opportunities

Tailoring Recommendations to Groups of Viewers on Smart TV: A Real-Time Profile Generation Approach

Predicting product advertisement links using hybrid learning within social networks

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