組合せバランスを意識したレシピ入替えを行うグループ向け献立推薦システムの提案

近年，投稿者が自由にレシピを作成し，多数の閲覧者に作成したレシピを公開することができる投稿型レシピサイトが登場している．投稿型レシピサイトに投稿された献立を利用するためには，ユーザは投稿された献立の中から自分の目的にあった献立を検索する必要がある．しかし，投稿型レシピサイトには膨大なコンテンツが存在するため，自分の目的にあった献立を見つけ出すことが困難になりつつある．　上記の問題の解決のため膨大なコンテンツの中からユーザの好みのコンテンツを見つけ出すための推薦システムの研究が進められている．さらに，投稿型レシピサイトには献立などグループで消費するコンテンツも存在しているため，グループを対象とした推薦システムの研究も必要とされている．多くのグループ向け推薦システムはグループに所属する個人のコンテンツへの好みを束ねあわせることによって推薦を実現する．しかし，献立を推薦する場合，献立は複数のレシピが組み合わさって成立しているため，グループのメンバー中に一人でも献立に含まれたレシピが嫌いなメンバーがいる場合，献立の全体の評価が下がってしまう．そのため，献立に含まれた嫌いなレシピ以外のレシピが活かされないという問題がある．　そこで，本研究ではグループのメンバーの材料と料理カテゴリの好みを基に，献立のレシピから嫌いなレシピを抽出し入替えを行い，レシピ入替えを行った献立を推薦することで，グループの推薦結果への満足度を向上させることを目標とする．本研究では献立のレシピ入替え手法として「類似度法」「平均値法」「最小値法」の3つの手法を提案した．最後に本研究ではアンケート調査により入替えをした献立のグループの満足度と，入替えをした献立の妥当性の2つの観点から評価を行った．その結果，本研究の提案手法のうち「最小値法」が最も有効な手法であることがわかった．　本研究は，多くの人が好ましいと感じるバランスを考慮しながら複数のコンテンツを組合せてグループ向けの推薦を行う点で新規性がある．電気通信大学201

SocialDining: Design and Analysis of a Group Recommendation Application in a Mobile Context

Mobile social networks are rapidly becoming an important new domain showcasing the power of mobile computing systems. These networks combine mobile location information with social networking data to enable fully context-aware environments. This paper describes SocialDining, a system that fuses mobile and social data to power novel context-aware recommendation services that provide recommendations to small groups of users who want to meet together for food or drink at local restaurants. We report our analysis on the data collected from 31 users for the SocialDining application over the course of 15 weeks

MobiGroup: Enabling Lifecycle Support to Social Activity Organization and Suggestion with Mobile Crowd Sensing

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This paper presents a group-aware mobile crowd sensing system called MobiGroup, which supports group activity organization in real-world settings. Acknowledging the complexity and diversity of group activities, this paper introduces a formal concept model to characterize group activities and classifies them into four organizational stages. We then present an intelligent approach to support group activity preparation, including a heuristic rule-based mechanism for advertising public activity and a context-based method for private group formation. In addition, we leverage features extracted from both online and offline communities to recommend ongoing events to attendees with different needs. Compared with the baseline method, people preferred public activities suggested by our heuristic rule-based method. Using a dataset collected from 45 participants, we found that the context-based approach for private group formation can attain a precision and recall of over 80%, and the usage of spatial-temporal contexts and group computing can have more than a 30% performance improvement over considering the interaction frequency between a user and related groups. A case study revealed that, by extracting the features such as dynamic intimacy and static intimacy, our cross-community approach for ongoing event recommendation can meet different user needs

Group Recommendation with Temporal Affinities

International audienceWe examine the problem of recommending items to ad-hoc user groups. Group recommendation in collaborative rating datasets has received increased attention recently and has raised novel challenges. Different consensus functions that aggregate the ratings of group members with varying semantics ranging from least misery to pairwise disagreement, have been studied. In this paper, we explore a new dimension when computing group recommendations, that is, affinity between group members and its evolution over time. We extend existing group recommendation semantics to include temporal affinity in recommendations and design GRECA, an efficient algorithm that produces temporal affinity-aware recommendations for ad-hoc groups. We run extensive experiments that show substantial improvements in group recommendation quality when accounting for affinity while maintaining very good performance

Group Recommendations: Survey and Perspectives

The popularity of group recommender systems has increased in the last years. More and more social activity is generated by users over the Web and thus not only domains as TV, music or holidays are used and researched anymore for group recommendation, but also collaborative learning support, digital libraries and other domains seems to be promising for group recommendation. Moreover, principles of group recommenders can be used in order to overcome some single user recommendation shortcomings, such as cold start problem. Numerous group recommenders have been proposed, they differ in application domains which are specific in group characteristics. Today's group recommenders do not include and use the power of social aspects (group structure, social status etc.), which can be extracted and derived from the group. We provide a survey of group recommendation principles for the Web domain and discuss trends and perspectives in this field