Comparison of group recommendation algorithms

In recent years recommender systems have become the common tool to handle the information overload problem of educational and informative web sites, content delivery systems, and online shops. Although most recommender systems make suggestions for individual users, in many circumstances the selected items (e.g., movies) are not intended for personal usage but rather for consumption in groups. This paper investigates how effective group recommendations for movies can be generated by combining the group members’ preferences (as expressed by ratings) or by combining the group members’ recommendations. These two grouping strategies, which convert traditional recommendation algorithms into group recommendation algorithms, are combined with five commonly used recommendation algorithms to calculate group recommendations for different group compositions. The group recommendations are not only assessed in terms of accuracy, but also in terms of other qualitative aspects that are important for users such as diversity, coverage, and serendipity. In addition, the paper discusses the influence of the size and composition of the group on the quality of the recommendations. The results show that the grouping strategy which produces the most accurate results depends on the algorithm that is used for generating individual recommendations. Therefore, the paper proposes a combination of grouping strategies which outperforms each individual strategy in terms of accuracy. Besides, the results show that the accuracy of the group recommendations increases as the similarity between members of the group increases. Also the diversity, coverage, and serendipity of the group recommendations are to a large extent dependent on the used grouping strategy and recommendation algorithm. Consequently for (commercial) group recommender systems, the grouping strategy and algorithm have to be chosen carefully in order to optimize the desired quality metrics of the group recommendations. The conclusions of this paper can be used as guidelines for this selection process.     

Personalizing recommendation diversity based on user personality

In recent years, diversity has attracted increasing attention in the field of recommender systems because of its ability of catching users’ various interests by providing a set of dissimilar items. There are few endeavors to personalize the recommendation diversity being tailored to individual users’ diversity needs. However, they mainly depend on users’ behavior history such as ratings to customize diversity, which has two limitations: (1) They neglect taking into account a user’s needs that are inherently caused by some personal factors such as personality; (2) they fail to work well for new users who have little behavior history. In order to address these issues, this paper proposes a generalized, dynamic personality-based greedy re-ranking approach to generating the recommendation list. On one hand, personality is used to estimate each user’s diversity preference. On the other hand, personality is leveraged to alleviate the cold-start problem of collaborative filtering recommendations. The experimental results demonstrate that our approach significantly outperforms related methods (including both non-diversity-oriented and diversity-oriented methods) in terms of metrics measuring recommendation accuracy and personalized diversity degree, especially in the cold-start setting.     

Collaborative user modeling with user-generated tags for social recommender systems

With the popularity of social media services, the sheer amount of content is increasing exponentially on the Social Web that leads to attract considerable attention to recommender systems. Recommender systems provide users with recommendations of items suited to their needs. To provide proper recommendations to users, recommender systems require an accurate user model that can reflect a user’s characteristics, preferences and needs. In this study, by leveraging user-generated tags as preference indicators, we propose a new collaborative approach to user modeling that can be exploited to recommender systems. Our approach first discovers relevant and irrelevant topics for users, and then enriches an individual user model with collaboration from other similar users. In order to evaluate the performance of our model, we compare experimental results with a user model based on collaborative filtering approaches and a vector space model. The experimental results have shown the proposed model provides a better representation in user interests and achieves better recommendation results in terms of accuracy and ranking.     

Personality,User Preferences and Behavior in Recommender systems

This paper reports on a study of 1840 users of the MovieLens recommender system with identified Big-5 personality types. Based on prior literature that suggests that personality type is a stable predictor of user preferences and behavior, we examine factors of user retention and engagement, content preferences, and rating patterns to identify recommender-system related behaviors and preferences that correlate with user personality. We find that personality traits correlate significantly with behaviors and preferences such as newcomer retention, intensity of engagement, activity types, item categories, consumption versus contribution, and rating patterns.     

基于多维度兴趣注意力和用户长短期偏好的新闻推荐

在这个网络媒体平台成为获取新闻资讯的主流方式的时代,新闻推荐扮演着至关重要的角色。一方面,媒体平台使用新闻推荐可帮助用户过滤掉不感兴趣的新闻,定制个性化阅读内容推送;另一方面,智能推送服务能够增加新闻点击率,帮助媒体平台实现新闻的快速传播。目前,新闻推荐逐渐成为数据分发领域核心技术之一,逐渐引起国内外学者的关注。该文针对新闻热度不均衡问题造成的长尾现象,提出了一种基于多维度兴趣注意力的用户长短期偏好的新闻推荐模型。首先,对用户长期偏好进行挖掘时把用户兴趣分成多个维度,并采用注意力机制控制不同兴趣维度的重要程度,从而学习到包含不同维度兴趣信息的长期偏好。其次,采用CNN与注意力网络相结合的模型对新闻进行表示学习,采用GRU在用户近段时间内的阅读历史中学习用户短期偏好。最后,通过大量在真实新闻数据集上的实验,以AUC、MRR、NDCG为评价指标与其他基线方法进行比较,证实了该模型均优于其他方法。     

RecAm: a collaborative context-aware framework for multimedia recommendations in an ambient intelligence environment

With an ever-increasing accessibility to different multimedia contents in real-time, it is difficult for users to identify the proper resources from such a vast number of choices. By utilizing the user’s context while consuming diverse multimedia contents, we can identify different personal preferences and settings. However, there is a need to reinforce the recommendation process in a systematic way, with context-adaptive information. The contributions of this paper are twofold. First, we propose a framework, called RecAm, which enables the collection of contextual information and the delivery of resulted recommendation by adapting the user’s environment using Ambient Intelligent (AmI) Interfaces. Second, we propose a recommendation model that establishes a bridge between the multimedia resources, user joint preferences, and the detected contextual information. Hence, we obtain a comprehensive view of the user’s context, as well as provide a personalized environment to deliver the feedback. We demonstrate the feasibility of RecAm with two prototypes applications that use contextual information for recommendations. The offline experiment conducted shows the improvement of delivering personalized recommendations based on the user’s context on two real-world datasets.     

Improving sparsity and new user problems in collaborative filtering by clustering the personality factors

In collaborative filtering recommender systems, items recommended to an active user are selected based on the interests of users similar to him/her. Collaborative filtering systems suffer from the ‘sparsity’ and ‘new user’ problems. The former refers to the insufficiency of data about users’ preferences and the latter addresses the lack of enough information about the new-coming user. Clustering users is an effective way to improve the performance of collaborative filtering systems in facing the aforementioned problems. In previous studies, users were clustered based on characteristics such as ratings given by them as well as their age, gender, occupation, and geographical location. On the other hand, studies show that there is a significant relationship between users’ personality traits and their interests. To alleviate the sparsity and new user problems, this paper presents a new collaborative filtering system in which users are clustered based on their ‘personality traits’. In the proposed method, the personality of each user is described according to the big-5 personality model and users with similar personality are placed in the same cluster using K-means algorithm. The unknown ratings of the sparse user-item matrix are then estimated based on the clustered users, and recommendations are found for a new user according to a user-based approach which relays on the interests of the users with similar personality to him/her. In addition, for an existing user in the system, recommendations are offered in an item-based approach in which the similarity of items is estimated based on the ratings of users similar to him/her in personality. The proposed method is compared to some former collaborative filtering systems. The results demonstrate that in facing the data sparsity and new user problems, this method reduces the mean absolute error and improves the precision of the recommendations.     

User preference mining techniques for personalized applications

Advanced personalized e-applications require comprehensive knowledge about their users’ likes and dislikes in order to provide individual product recommendations, personal customer advice, and custom-tailored product offers. In our approach we model such preferences as strict partial orders with “A is better than B” semantics, which has been proven to be very suitable in various e-applications. In this paper we present preference mining techniques for detecting strict partial order preferences in user log data. Real-life e-applications like online shops or financial services usually have large log data sets containing the transactions of their customers. Since the preference miner uses sophisticated SQL operations to execute all data intensive operations on database layer, our algorithms scale well even for such large log data sets. With preference mining personalized e-applications can gain valuable knowledge about their customers’ preferences, which can be applied for personalized product recommendations, individual customer service, or one-to-one marketing.     

An OWA‐Based Ranking Approach for University Books Recommendation

Generally the book recommendation approaches are personalized in nature, that is, they utilize the users’ purchasing behavior to recommend them the book similar to their preferences. The main problem with the personalized recommendation is its knowledge requirement about users’ past preferences. As a result, these techniques fail in producing appropriate recommendation for a new user whose preferences are not known. The personalized recommendation also needs extra space to store the users’ preferences. In this paper, a framework to recommend books to university students for their studies is presented. In order to answer which books are to be included in the syllabus, a specialized way of recommendation, where recommendations from experts of the subjects at different universities are considered, is presented. We have suggested a ranked recommendation approach for books, which employ Ordered Weighted Aggregation (OWA), a fuzzy‐based aggregation, to aggregate the several ranking of the top universities. On the one hand, it does not need user prior preferences, and on the other hand, it eases the complexities of personalized recommendation to huge number of users and replaces it with a single ranked recommendation. The experimental results are compared with the existing positional aggregation algorithm that demonstrates significant improvement in the results with respect to various performance metrics.     

Entertainment recommender systems for group of users

Recommender systems are used to recommend potentially interesting items to users in different domains. Nowadays, there is a wide range of domains in which there is a need to offer recommendations to group of users instead of individual users. As a consequence, there is also a need to address the preferences of individual members of a group of users so as to provide suggestions for groups as a whole. Group recommender systems present a whole set of new challenges within the field of recommender systems. In this article, we present two expert recommender systems that suggest entertainment to groups of users. These systems, jMusicGroupRecommender and jMoviesGroupRecommender, suggest music and movies and utilize different methods for the generation of group recommendations: merging recommendations made for individuals, aggregation of individuals’ ratings, and construction of group preference models. We also describe the results obtained when comparing different group recommendation techniques in both domains.     

A recommender system for the TV on the web: integrating unrated reviews and movie ratings

The activity of Social-TV viewers has grown considerably in the last few years—viewers are no longer passive elements. The Web has socially empowered the viewers in many new different ways, for example, viewers can now rate TV programs, comment them, and suggest TV shows to friends through Web sites. Some innovations have been exploring these new activities of viewers but we are still far from realizing the full potential of this new setting. For example, social interactions on the Web, such as comments and ratings in online forums, create valuable feedback about the targeted TV entertainment shows. In this paper, we address this last setting: a media recommendation algorithm that suggests recommendations based on users’ ratings and unrated comments. In contrast to similar approaches that are only ratings-based, we propose the inclusion of sentiment knowledge in recommendations. This approach computes new media recommendations by merging media ratings and comments written by users about specific entertainment shows. This contrasts with existing recommendation methods that explore ratings and metadata but do not analyze what users have to say about particular media programs. In this paper, we argue that text comments are excellent indicators of user satisfaction. Sentiment analysis algorithms offer an analysis of the users’ preferences in which the comments may not be associated with an explicit rating. Thus, this analysis will also have an impact on the popularity of a given media show. Thus, the recommendation algorithm—based on matrix factorization by Singular Value Decomposition—will consider both explicit ratings and the output of sentiment analysis algorithms to compute new recommendations. The implemented recommendation framework can be integrated on a Web TV system where users can view and comment entertainment media from a video-on-demand service. The recommendation framework was evaluated on two datasets from IMDb with 53,112 reviews (50 % unrated) and Amazon entertainment media with 698,210 reviews (26 % unrated). Recommendation results with ratings and the inferred preferences—based on the sentiment analysis algorithms—exhibited an improvement over the ratings only based recommendations. This result illustrates the potential of sentiment analysis of user comments in recommendation systems.     

基于聚类和用户偏好的协同过滤推荐算法

协同过滤推荐算法使用评分数据作为学习的数据源,针对协同过滤推荐算法中存在的评分数据稀疏以及算法的可拓展性问题,提出了一种基于聚类和用户偏好的协同过滤推荐算法。为了挖掘用户的偏好,该算法引入了用户对项目类型的平均评分到评分矩阵中,并加入了基于用户自身属性的相似度;同时,为了降低数据稀疏性,该算法使用Weighted Slope One算法填充评分数据中的未评分项,并通过融入密度和距离优化初始聚类中心的K-means算法聚类填充后的评分数据中的用户,缩小了相似用户的搜索空间;最后在聚类后的数据集中使用传统的协同过滤推荐算法生成目标用户的推荐结果。通过使用MovieLens100K数据集实验证明,提出的算法对推荐效果有所改善。     

Scratch my back and I'll scratch yours: The impact of user effort and recommendation agent effort on perceived recommendation agent quality

Product recommendation agents (RAs) assist online firms to adapt their suggested offers to users’ preferences, thereby lowering users’ decision effort. The concept of effort is central in decision-making, yet it remains unclear whether it should be regarded as a cost or as a benefit improving the odds of a better decision. Building on Social Exchange theory, we suggest that interactions between users and RAs are driven by the concepts of perceived interdependence and reciprocity. We suggest that perceived user effort decreases the perceived RA quality, whereas RA effort increases the perceived RA quality. We conducted two experimental studies across different contexts. We found that users evaluate RAs based on their own expended effort, in relation to how much effort they perceived the RA has put into the process of generating recommendations. Such an effect is attenuated by users’ familiarity with the product context. Our findings offer important insights into how online firms can improve the use of their RAs.     

Finding users preferences from large-scale online reviews for personalized recommendation

Along with the growth of Internet and electronic commerce, online consumer reviews have become a prevalent and rich source of information for both consumers and merchants. Numerous reviews record massive consumers’ opinions on products or services, which offer valuable information about users’ preferences for various aspects of different entities. This paper proposes a novel approach to finding the user preferences from free-text online reviews, where a user-preference-based collaborative filtering approach, namely UPCF, is developed to discover important aspects to users, as well as to reflect users’ individual needs for different aspects for recommendation. Extensive experiments are conducted on the data from a real-world online review platform, with the results showing that the proposed approach outperforms other approaches in effectively predicting the overall ratings of entities to target users for personalized recommendations. It also demonstrates that the approach has an advantage in dealing with sparse data, and can provide the recommendation results with desirable understandability.     

Effective methods for increasing aggregate diversity in recommender systems

In order to make a recommendation, a recommender system typically first predicts a user’s ratings for items and then recommends a list of items to the user which have high predicted ratings. Quality of predictions is measured by accuracy, that is, how close the predicted ratings are to actual ratings. On the other hand, quality of recommendation lists is evaluated from more than one perspective. Since accuracy of predicted ratings is not enough for customer satisfaction, metrics such as novelty, serendipity, and diversity are also used to measure the quality of the recommendation lists. Aggregate diversity is one of these metrics which measures the diversity of items across the recommendation lists of all users. Increasing aggregate diversity is important because it leads a more even distribution of items in the recommendation lists which prevents the long-tail problem. In this study, we propose two novel methods to increase aggregate diversity of a recommender system. The first method is a reranking approach which takes a ranked list of recommendations of a user and reranks it to increase aggregate diversity. While the reranking approach is applied after model generation as a wrapper the second method is applied in model generation phase which has the advantage of being more efficient in the generation of recommendation lists. We compare our methods with the well-known methods in the field and show the superiority of our methods using real-world datasets.     

Generating recommendations for consensus negotiation in group personalization services

There are increasingly many personalization services in ubiquitous computing environments that involve a group of users rather than individuals. Ubiquitous commerce is one example of these environments. Ubiquitous commerce research is highly related to recommender systems that have the ability to provide even the most tentative shoppers with compelling and timely item suggestions. When the recommendations are made for a group of users, new challenges and issues arise to provide compelling item suggestions. One of the challenges a group recommender system must cope with is the potentially conflicting preferences of multiple users when selecting items for recommendation. In this paper, we focus on how individual user models can be aggregated to reach a consensus on recommendations. We describe and evaluate nine different consensus strategies and analyze them to highlight the benefits of group recommendation using live-user preference data. Moreover, we show that the performance is significantly different among strategies.     

一种融合个性化与多样性的人物标签推荐方法

针对人物标签推荐中多样性及推荐标签质量问题,该文提出了一种融合个性化与多样性的人物标签推荐方法。该方法使用主题模型对用户关注对象建模,通过聚类分析把具有相似言论的对象划分到同一类簇;然后对每个类簇的标签进行冗余处理,并选取代表性标签;最后对不同类簇中的标签融合排序,以获取Top-K个标签推荐给用户。实验结果表明,与已有推荐方法相比,该方法在反映用户兴趣爱好的同时,能显著提高标签推荐质量和推荐结果的多样性。     

一种基于用户播放行为序列的个性化视频推荐策略

本文针对在线视频服务网站的个性化推荐问题,提出了一种基于用户播放行为序列的个性化推荐策略.该策略通过深度神经网络词向量模型分析用户播放视频行为数据,将视频映射成等维度的特征向量,提取视频的语义特征.聚类用户播放历史视频的特征向量,建模用户兴趣分布矩阵.结合用户兴趣偏好和用户观看历史序列生成推荐列表.在大规模的视频服务系统中进行了离线实验,相比随机算法、基于物品的协同过滤和基于用户的协同过滤传统推荐策略,本方法在用户观看视频的Top-N推荐精确率方面平均分别获得22.3%、30.7%和934%的相对提升,在召回率指标上分别获得52.8%、41%和1065%的相对提升.进一步地与矩阵分解算法SVD++、基于双向LSTM模型和注意力机制的Bi-LSTM+Attention算法和基于用户行为序列的深度兴趣网络DIN比较,Top-N推荐精确率和召回率也得到了明显提升.该推荐策略不仅获得了较高的精确率和召回率,还尝试解决传统推荐面临大规模工业数据集时的数据要求严苛、数据稀疏和数据噪声等问题.     

基于社交关系和用户偏好的多样性图推荐方法

以协同过滤为代表的传统推荐算法能够为用户提供准确率较高的推荐列表,但忽略了推荐系统中另外一个重要的衡量标准:多样性。随着社交网络的日益发展,大量冗余和重复的信息充斥其间,信息过载使得快速、有效地发现用户的兴趣爱好变得更加困难。针对某个用户推荐最能满足其兴趣爱好的物品,需要具备显著的相关度且能覆盖用户广泛的兴趣爱好。因此,基于社交关系和用户偏好提出一种面向多样性和相关度的图排序框架。首先,引入社交关系图模型,综合考虑用户及物品之间的关系,以更好地建模它们的相关度;然后,利用线性模型融合多样性和相关性两个重要指标;最后,利用Spark GraphX并行图计算框架实现该算法,并在真实的数据集上通过实验验证所提方法的有效性和扩展性。     

融合用户属性与项目流行度的用户冷启动推荐模型

冷启动一直是推荐系统领域中被密切关注的问题,针对新注册用户冷启动的问题,文中提出了一种融合用户人口统计学信息与项目流行的推荐模型.首先对训练集用户进行聚类,将训练集用户划分为若干类.然后计算新用户与所属类别中其他用户之间的距离,选择其近邻用户集,在评分计算时综合考虑项目流行度对推荐效果的影响,进而为目标用户推送感兴趣的...