Novel of Web Search Strategy Based on Web Page Block Granularity Analysis Algorithm and Correlation Calculation Model

Fan, Ganglong; Xu, Hongsheng

doi:10.1007/978-3-030-15235-2_75

Cited by 1 publication

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“…The higher precision and recall show better recommendation performance. The precision, recall, and F1-score are calculated by equation (2) equation 3, and equation (4) respectively [66].…”

Section: ) Better Precision Recall and F-scorementioning

confidence: 99%

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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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“…The higher precision and recall show better recommendation performance. The precision, recall, and F1-score are calculated by equation (2) equation 3, and equation (4) respectively [66].…”

Section: ) Better Precision Recall and F-scorementioning

confidence: 99%

“…In equation (2) and 3, the R r is the recommendation results, and T u is the set of user's favorite items. To find the best blend of recall and precision, we calculated the F1-Score by equation 4, which is the harmonic mean of both precision and recall metrics.…”

Section: ) Better Precision Recall and F-scorementioning

confidence: 99%

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

Alam

Khusro

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

Novel of Web Search Strategy Based on Web Page Block Granularity Analysis Algorithm and Correlation Calculation Model

Cited by 1 publication

References 1 publication

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

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

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