語句間の意味構造に基づくニュース記事推薦システムの提案

近年，ユーザの行動やソーシャルメディア上での発言を興味関心として分析し，ニュース記事を推薦するキュレーションサービスが普及している．膨大な情報から自分で必要なものを探さなくても，自身の興味に沿った情報が手に入ることで利用者が増加している．既存のコンテンツベースの情報推薦システムに関する研究では記事推薦のために各語句を特徴としているが，頻出する語句を重要視しており語句間の関係を特徴として用いていない．本研究は，ユーザが興味関心を示す記事に表れる語句間の意味構造を用いることで，ユーザが面白いと感じることができるニュース記事を収集，推薦するシステムを提案する．本研究では面白いニュース記事をユーザが興味を示すことができ，意外な情報が得られるものと定義した．語句間の意味構造Linked Dataで表現する．同ニュース記事の同文脈に表れる複数の語句間の意味構造を文構造と定義する．ユーザが興味・関心を示す記事文の文構造の部分グラフを用いることでインターネット上のニュース記事を推薦する手法を提案する．本手法の有効性を確かめるため，20人の被験者に提案手法，ベースライン手法それぞれによるニュース記事推薦をして評価を得る比較実験を行った．ベースライン手法は単語の重要度を出現頻度から計算するtf-idfを用いた．提案手法によるニュース記事推薦での関連度の指標の平均値は4点満点中3.06，興味度は3.30，意外度は2.93という結果であった．ベースライン手法では関連度が3.22，興味度が3.03，意外度が2.79という結果であった．ベースライン手法との比較実験により，提案手法は推薦するニュース記事の関連度は下がるものの，ユーザが興味を持つことができ，また意外と感じることができるニュース記事推薦手法であることがわかった．これによりユーザに面白い記事を推薦できる手法として提案手法は有効であることが明らかになった．電気通信大学201

A Linked Data Recommender System Using a Neighborhood-Based Graph Kernel

Abstract. The ultimate mission of a Recommender System (RS) is to help users discover items they might be interested in. In order to be really useful for the end-user, Content-based (CB) RSs need both to harvest as much information as possible about such items and to effectively han-dle it. The boom of Linked Open Data (LOD) datasets with their huge amount of semantically interrelated data is thus a great opportunity for boosting CB-RSs. In this paper we present a CB-RS that leverages LOD and profits from a neighborhood-based graph kernel. The proposed ker-nel is able to compute semantic item similarities by matching their local neighborhood graphs. Experimental evaluation on the MovieLens dataset shows that the proposed approach outperforms in terms of accuracy and novelty other competitive approaches.

A Systematic Literature Review of Linked Data-based Recommender Systems

Recommender Systems (RS) are software tools that use analytic technologies to suggest different items of interest to an end user. Linked Data is a set of best practices for publishing and connecting structured data on the Web. This paper presents a systematic literature review to summarize the state of the art in recommender systems that use structured data published as Linked Data for providing recommendations of items from diverse domains. It considers the most relevant research problems addressed and classifies RS according to how Linked Data has been used to provide recommendations. Furthermore, it analyzes contributions, limitations, application domains, evaluation techniques, and directions proposed for future research. We found that there are still many open challenges with regard to RS based on Linked Data in order to be efficient for real applications. The main ones are personalization of recommendations; use of more datasets considering the heterogeneity introduced; creation of new hybrid RS for adding information; definition of more advanced similarity measures that take into account the large amount of data in Linked Data datasets; and implementation of testbeds to study evaluation techniques and to assess the accuracy scalability and computational complexity of RS

RecRules: Recommending IF-THEN Rules for End-User Development

Nowadays, end users can personalize their smart devices and web applications by defining or reusing IF-THEN rules through dedicated End-User Development (EUD) tools. Despite apparent simplicity, such tools present their own set of issues. The emerging and increasing complexity of the Internet of Things, for example, is barely taken into account, and the number of possible combinations between triggers and actions of different smart devices and web applications is continuously growing. Such a large design space makes end-user personalization a complex task for non-programmers, and motivates the need of assisting users in easily discovering and managing rules and functionality, e.g., through recommendation techniques. In this paper, we tackle the emerging problem of recommending IF-THEN rules to end users by presenting RecRules, a hybrid and semantic recommendation system. Through a mixed content and collaborative approach, the goal of RecRules is to recommend by functionality: it suggests rules based on their final purposes, thus overcoming details like manufacturers and brands. The algorithm uses a semantic reasoning process to enrich rules with semantic information, with the aim of uncovering hidden connections between rules in terms of shared functionality. Then, it builds a collaborative semantic graph, and it exploits different types of path-based features to train a learning to rank algorithm and compute top-N recommendations. We evaluate RecRules through different experiments on real user data extracted from IFTTT, one of the most popular EUD tool. Results are promising: they show the effectiveness of our approach with respect to other state-of-the-art algorithms, and open the way for a new class of recommender systems for EUD that take into account the actual functionality needed by end users

Executing, Comparing, and Reusing Linked Data-Based Recommendation Algorithms With the Allied Framework

International audienceData published on the Web following the Linked Data principles has resulted in a global data space called the Web of Data. These principles led to semantically interlink and connect different resources at data level regardless their structure, authoring, location, etc. The tremendous and continuous growth of the Web of Data also implies that now it is more likely to find resources that describe real-life concepts. However, discovering and recommending relevant related resources is still an open research area. This chapter studies recommender systems that use Linked Data as a source containing a significant amount of available resources and their relationships useful to produce recommendations. Furthermore, it also presents a framework to deploy and execute state-of-the-art algorithms for Linked Data that have been re-implemented to measure and benchmark them in different application domains and without being bound to a unique dataset

Machine Learning Models for Context-Aware Recommender Systems

The mass adoption of the internet has resulted in the exponential growth of products and services on the world wide web. An individual consumer, faced with this data deluge, is expected to make reasonable choices saving time and money. Organizations are facing increased competition, and they are looking for innovative ways to increase revenue and customer loyalty. A business wants to target the right product or service to an individual consumer, and this drives personalized recommendation. Recommender systems, designed to provide personalized recommendations, initially focused only on the user-item interaction. However, these systems evolved to provide a context-aware recommendations. Context-aware recommender systems utilize additional context, such as genre for movie recommendation, while recommending items to users. Latent factor methods have been a popular choice for recommender systems. With the resurgence of neural networks, there has also been a trend towards applying deep learning methods to recommender systems. This study proposes a novel contextual latent factor model that is capable of utilizing the context from a dual-perspective of both users and items. The proposed model, known as the Group-Aware Latent Factor Model (GLFM), is applied to the event recommendation task. The GLFM model is extensible, and it allows other contextual attributes to be easily be incorporated into the model. While latent-factor models have been extremely popular for recommender systems, they are unable to model the complex non-linear user-item relationships. This has resulted in the interest in applying deep learning methods to recommender systems. This study also proposes another novel method based on the denoising autoencoder architecture, which is referred to as the Attentive Contextual Denoising Autoencoder (ACDA). The ACDA model augments the basic denoising autoencoder with a context-driven attention mechanism to provide personalized recommendation. The ACDA model is applied to the event and movie recommendation tasks. The effectiveness of the proposed models is demonstrated against real-world datasets from Meetup and Movielens, and the results are compared against the current state-of-the-art baseline methods