Socially aware tv program recommender for multiple viewers

Shin, Choonsung; Woo, Woontack

doi:10.1109/tce.2009.5174476

Cited by 53 publications

(28 citation statements)

References 11 publications

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“…based on tensor factorization [24,23] incorporate time [26,45,9] or location [14,28] as additional parameters to encode contextual information. In addition, some advocate to model the local social environment [43,30] and propose strategies to improve recommendations in households with multiple users sharing a single device [38].…”

Section: Contextual Factors In Recommendersmentioning

confidence: 99%

Countering Contextual Bias in TV Watching Behavior

Lorenz

Jing

Lommatzsch

et al. 2017

Proceedings of the 2017 ACM International Conference on Interactive Experiences for TV and Online Video

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Context-awareness has become a critical factor in improving the predictions of user interest in modern online TV recommendation systems. In addition to individual user preferences, existing context-aware approaches such as tensor factorization incorporate system-level contextual bias to increase predicting accuracy. We analyzed a user interaction dataset from a WebTV platform, and identified that such contextual bias creates a skewed selection of recommended programs which ultimately locks users in a filter bubble. To address this issue, we introduce the Twitter social stream as a source of external context to extend the choice with items related to social media events. We apply two trend indicators, Trend Momentum and SigniScore, to the Twitter histories of relevant programs. The evaluation reveals that Trend Momentum outperforms SigniScore and signalizes 96% of all peaks ahead of time regarding the selected candidate program titles.

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Section: Contextual Factors In Recommendersmentioning

confidence: 99%

Countering Contextual Bias in TV Watching Behavior

Lorenz

Jing

Lommatzsch

et al. 2017

Proceedings of the 2017 ACM International Conference on Interactive Experiences for TV and Online Video

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“…Group profiles therefore describe common attributes used by individual conference participants. To generate group profiles consisting of individual conference participants, we adopt the socially aware TV programme recommender approach used in [39] and initially combine individual participant profiles based on their research paper interests through collaborative tagging (folksonomies). We also adopt the user -resource -tag relational model in [47] to create folksonomies among the conference participants through scholarly papers and tags.…”

Section: Group Profile Generationmentioning

confidence: 99%

Scholarly paper recommendation based on social awareness and folksonomy

Asabere

Xia

Meng

et al. 2014

International Journal of Parallel, Emergent and Distributed Sys

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The significant proliferation of research papers in both conferences and journals has made it difficult for researchers to easily access relevant scholarly papers for academic learning. This has been a substantial problem for many researchers. Conferences, in comparison with journals, have an aspect of social learning and networking, which leads to personal familiarisation through various interactions among researchers. In this paper, we improve the social awareness of conference participants by proposing a novel folksonomy-based paper recommendation algorithm, called socially aware recommendation of scholarly papers (SARSP). SARSP recommends papers issued by active participants (APs), to other Group Profile Participants at the same conference based on preference similarity of their research interests. In addition, SARSP computes the social ties between an AP and other conference participants to effectively generate social recommendations of scholarly papers. We evaluate our proposed algorithm using a real-world data-set. Our experimental results confirm that SARSP has significant improvement over other existing methods.

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“…SongJie Gong proposed a collaborative filtering recommendation algorithm based on user clustering and item clustering .Manos Papagelis, Dimitris Plexousakis have done qualitative analysis of user based and item based prediction algorithms for recommendation agents. [6,24,31,32,33,34].As such, even though various strategies have been developed in attempts to assist the selection of TV programs for a group of users, individual preferences and at most user priority are mainly used in generating lists of preferred programs and in final program selection. In addition to the personalization our proposed system uses societal structure of family to make TV more social and user friendly.…”

Section: Marilyn (-Multimodal Avatar Responsive Live Newsmentioning

confidence: 99%

Family Aware TV Program and Settings Recommender

Gs¹,

Kulkarni²

2011

IJCA

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In this paper we are proposing a design of TV program and settings recommendation engine utilizing contextual parameters like personal, social, temporal, mood and activity. In addition to the contextual parameters the system utilize the explicit or implicit user ratings and watching history to resolve the conflict if any while recommending the services .The System is implemented exploiting AI techniques ( like ontology, fuzzy logic ,Bayesian classifier and Rule Base) , RDBMS and SQL Query Processing . The motivation behind the proposed work is i) to improve the user's satisfaction level and ii) to improve the social relationship between user and TV. The context aware recommender utilizes social context data as an additional input to the recommendation task alongside information of users and tv programs. We have analyzed the recommendation process and performed a subjective test to show the usefulness of the proposed system for small families.

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Socially aware tv program recommender for multiple viewers

Cited by 53 publications

References 11 publications

Countering Contextual Bias in TV Watching Behavior

Countering Contextual Bias in TV Watching Behavior

Scholarly paper recommendation based on social awareness and folksonomy

Family Aware TV Program and Settings Recommender

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