Feature-based and Clique-based User Models for Movie Selection: A Comparative Study

The huge amount of information available in the currently evolving world wide information infrastructure at any one time can easily overwhelm end-users. One way to address the information explosion is to use an ‘information filtering agent’ which can select information according to the interest and/or need of an end-user. However, at present few information filtering agents exist for the evolving world wide multimedia information infrastructure. In this study, we evaluate the use of feature-based approaches to user modeling with the purpose of creating a filtering agent for the video-on-demand application. We evaluate several feature and clique-based models for 10 voluntary subjects who provided ratings for the movies. Our preliminary results suggest that feature-based selection can be a useful tool to recommend movies according to the taste of the user and can be as effective as a movie rating expert. We compare our feature-based approach with a clique-based approach, which has advantages where information from other users is available. This revised version was published online in July 2006 with corrections to the Cover Date.     

基于用户行为模型和蚁群聚类的协同过滤推荐算法

协同过滤技术是推荐系统中应用最为广泛的技术之一,用户的相似性度量是整个算法的核心要素,会对推荐算法准确率产生很大的影响.传统的协同过滤算法过度依赖用户评分机制,影片自身的标签信息没有被考虑为一个影响因素,在用户聚类时采用K近邻算法,会由于评分矩阵过于稀疏而难以收敛.同时,传统推荐技术仅基于用户历史行为进行推荐,无法为新用户提供合理的推荐.针对以上问题,提出了一种基于用户行为建模的蚁群聚类和协同过滤算法相结合的影片推荐技术.     

An improved collaborative movie recommendation system using computational intelligence

Recommendation systems have become prevalent in recent years as they dealing with the information overload problem by suggesting users the most relevant products from a massive amount of data. For media product, online collaborative movie recommendations make attempts to assist users to access their preferred movies by capturing precisely similar neighbors among users or movies from their historical common ratings. However, due to the data sparsely, neighbor selecting is getting more difficult with the fast increasing of movies and users. In this paper, a hybrid model-based movie recommendation system which utilizes the improved K-means clustering coupled with genetic algorithms (GAs) to partition transformed user space is proposed. It employs principal component analysis (PCA) data reduction technique to dense the movie population space which could reduce the computation complexity in intelligent movie recom-mendation as well. The experiment results on Movielens dataset indicate that the proposed approach can provide high performance in terms of accuracy, and generate more reliable and personalized movie recommendations when compared with the existing methods.     

Exploiting interactions of review text,hidden user communities and item groups,and time for collaborative filtering

Rich side information concerning users and items are valuable for collaborative filtering (CF) algorithms for recommendation. For example, rating score is often associated with a piece of review text, which is capable of providing valuable information to reveal the reasons why a user gives a certain rating. Moreover, the underlying community and group relationship buried in users and items are potentially useful for CF. In this paper, we develop a new model to tackle the CF problem which predicts user’s ratings on previously unrated items by effectively exploiting interactions among review texts as well as the hidden user community and item group information. We call this model CMR (co-clustering collaborative filtering model with review text). Specifically, we employ the co-clustering technique to model the user community and item group, and each community–group pair corresponds to a co-cluster, which is characterized by a rating distribution in exponential family and a topic distribution. We have conducted extensive experiments on 22 real-world datasets, and our proposed model CMR outperforms the state-of-the-art latent factor models. Furthermore, both the user’s preference and item profile are drifting over time. Dynamic modeling the temporal changes in user’s preference and item profiles are desirable for improving a recommendation system. We extend CMR and propose an enhanced model called TCMR to consider time information and exploit the temporal interactions among review texts and co-clusters of user communities and item groups. In this TCMR model, each community–group co-cluster is characterized by an additional beta distribution for time modeling. To evaluate our TCMR model, we have conducted another set of experiments on 22 larger datasets with wider time span. Our proposed model TCMR performs better than CMR and the standard time-aware recommendation model on the rating score prediction tasks. We also investigate the temporal effect on the user–item co-clusters.     

Collaborative filtering and deep learning based recommendation system for cold start items

Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.     

A semantic approach to remove incoherent items from a user profile and improve the accuracy of a recommender system

Recommender systems usually suggest items by exploiting all the previous interactions of the users with a system (e.g., in order to decide the movies to recommend to a user, all the movies she previously purchased are considered). This canonical approach sometimes could lead to wrong results due to several factors, such as a change in user preferences over time, or the use of her account by third parties. This kind of incoherence in the user profiles defines a lower bound on the error the recommender systems may achieve when they generate suggestions for a user, an aspect known in literature as magic barrier. This paper proposes a novel dynamic coherence-based approach to define the user profile used in the recommendation process. The main aim is to identify and remove, from the previously evaluated items, those not semantically adherent to the others, in order to make a user profile as close as possible to the user’s real preferences, solving the aforementioned problems. Moreover, reshaping the user profile in such a way leads to great advantages in terms of computational complexity, since the number of items considered during the recommendation process is highly reduced. The performed experiments show the effectiveness of our approach to remove the incoherent items from a user profile, increasing the recommendation accuracy.     

融合信任因子的多样化电影推荐算法

传统的协同过滤算法过于依赖用户之间的评分,容易出现冷启动和数据稀疏性问题,同时推荐结果单一,针对以上问题,本文提出了一种融合信任因子的多样化电影推荐算法.首先对用户相似度计算方法进行改进,引入用户间信任度关系和属性特征信息.接着使用聚类方法把具有相同兴趣的用户划分在同一社群.最后在评分时综合考虑用户活跃度对电影的推荐度,引入惩罚因子,从而为目标用户提供个性化、多样化的电影推荐.实验结果表明,本文提出的算法在推荐精度和多样性指标上均有所提高,有较好的推荐效果.     

Interactive resource recommendation algorithm based on tag information

With the popularization of social media and the exponential growth of information generated by online users, the recommender system has been popular in helping users to find the desired resources from vast amounts of data. However, the cold-start problem is one of the major challenges for personalized recommendation. In this work, we utilized the tag information associated with different resources, and proposed a tag-based interactive framework to make the resource recommendation for different users. During the interaction, the most effective tag information will be selected for users to choose, and the approach considers the users’ feedback to dynamically adjusts the recommended candidates during the recommendation process. Furthermore, to effectively explore the user preference and resource characteristics, we analyzed the tag information of different resources to represent the user and resource features, considering the users’ personal operations and time factor, based on which we can identify the similar users and resource items. Probabilistic matrix factorization is employed in our work to overcome the rating sparsity, which is enhanced by embedding the similar user and resource information. The experiments on real-world datasets demonstrate that the proposed algorithm can get more accurate predictions and higher recommendation efficiency.     

Personalized Tag Recommendation Using Social Influence

Tag recommendation encourages users to add more tags in bridging the semantic gap between human concept and the features of media object,which provides a feasible solution for content-based multimedia information retrieval.In this paper,we study personalized tag recommendation in a popular online photo sharing site - Flickr.Social relationship information of users is collected to generate an online social network.From the perspective of network topology,we propose node topological potential to characterize user’s social influence.With this metric,we distinguish different social relations between users and find out those who really have influence on the target users.Tag recommendations are based on tagging history and the latent personalized preference learned from those who have most influence in user’s social network.We evaluate our method on large scale real-world data.The experimental results demonstrate that our method can outperform the non-personalized global co-occurrence method and other two state-of-the-art personalized approaches using social networks.We also analyze the further usage of our approach for the cold-start problem of tag recommendation.     

Spark平台下基于上下文信息的影片混合推荐

响应速度较慢和推荐内容与用户上下文信息匹配程度低是当前影片推荐系统迫切需要解决的问题。针对上述挑战,提出Spark平台下基于上下文信息的影片混合推荐方法。它利用分布式并行计算技术Spark进行加速,来提高系统对于海量数据的检索与计算速度,从而减少了系统响应时间。同时该方法将“上下文推荐”和“交替最小二乘的协同过滤（ALS）”融合成一种混合推荐方法,提高了系统的推荐精度。实验结果表明,所提出的混合推荐方法有不错的效果。     

Enhancing recommender systems by incorporating social information

Although recommendation techniques have achieved distinct developments over the decades,the data sparseness problem of the involved user-item matrix still seriously influences the recommendation quality.Most of the existing techniques for recommender systems cannot easily deal with users who have very few ratings.How to combine the increasing amount of different types of social information such as user generated content and social relationships to enhance the prediction precision of the recommender systems remains a huge challenge.In this paper,based on a factor graph model,we formalize the problem in a semi-supervised probabilistic model,which can incorporate different user information,user relationships,and user-item ratings for learning to predict the unknown ratings.We evaluate the method in two different genres of datasets,Douban and Last.fm.Experiments indicate that our method outperforms several state-of-the-art recommendation algorithms.Furthermore,a distributed learning algorithm is developed to scale up the approach to real large datasets.     

基于局部模型加权融合的Top-N电影推荐算法

为了解决传统推荐算法使用单一模型无法准确捕获用户偏好的问题, 将稀疏线性模型作为基本推荐模型,提出了基于用户聚类的局部模型加权融合算法来实现电影的Top-N个性化推荐。同时,为了实现用户聚类,文中利用LDA主题模型和电影的文本内容信息,提出了语义层次用户特征向量的计算方法,并基于此来实现用户聚类。在豆瓣网电影数据集上的实验验证结果表明,所提局部加权融合推荐算法提升了原始基模型的推荐效果,同时又优于一些传统的经典推荐算法,从而证明了该推荐算法的有效性。     

Incorporating contextual information and collaborative filtering methods for multimedia recommendation in a mobile environment

Recommender systems have been developed in different application services. In addition to using recommendation techniques, it is helpful to employ contextual information in determining the relevance of an item to a users’s needs. To enhance recommendation performance, we present in this study two approaches that, in a direct way, integrate different types of contextual information and user ratings in computational methods. To verify the proposed approaches in making collaborative recommendations, we conduct a series of experiments to evaluate performance. The results show that the proposed context-aware methods outperform other conventional approaches. Moreover, we implement a mobile multimedia recommendation system on a cloud platform to demonstrate how our approaches can be used to develop a real-world application.     

一种电影推荐模型：解决冷启动问题

推荐系统数据库的评分数据稀少,对电影推荐的质量有所限制。为解决这个问题,提出一种同时将用户和电影元数据纳入改进的隐语义模型的模型。构造用户元数据-分类矩阵与电影元数据-分类矩阵,将分类域与隐因子空间进行映射,以此获取新用户与新电影的隐因子,进行推荐。实验结果表明,这种模型在提高预测准确率的同时,有效地解决了冷启动问题。     

Social recommendation based on trust and influence in SNS environments

The development of social media provides convenience to people’s lives. People’s social relationship and influence on each other is an important factor in a variety of social activities. It is obviously important for the recommendation, while social relationship and user influence are rarely taken into account in traditional recommendation algorithms. In this paper, we propose a new approach to personalized recommendation on social media in order to make use of such a kind of information, and introduce and define a set of new measures to evaluate trust and influence based on users’ social relationship and rating information. We develop a social recommendation algorithm based on modeling of users’ social trust and influence combined with collaborative filtering. The optimal linear relation between them will be reached by the proposed method, because the importance of users’ social trust and influence varies with the data. Our experimental results show that the proposed algorithm outperforms traditional recommendation in terms of recommendation accuracy and stability.

     

基于遗忘函数的均值贝叶斯个性化排序算法研究

针对贝叶斯个性化排序算法未能充分应用用户的行为信息,导致算法在数据稀疏情况下推荐性能以及鲁棒性均大幅度降低的问题,提出了均值贝叶斯个性化排序(MBPR)算法,来进一步挖掘用户对隐式反馈信息的偏好关系。考虑到用户兴趣随时间变化的特征,又将遗忘函数引入MBPR算法中。该算法首先对用户的历史评分记录进行预处理;然后根据用户的评分信息对项目进行正负反馈的划分,对每名用户进行个性化建模,挖掘用户对未参与项目的喜好程度,生成推荐列表。为验证提出算法的推荐性能,在公开数据集MovieLens及Yahoo上进行分析和对比实验。实验结果表明该算法的推荐性能及鲁棒性较对比算法均有显著提高。     

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.     

EARS: Emotion-aware recommender system based on hybrid information fusion

Recommender systems suggest items that users might like according to their explicit and implicit feedback information, such as ratings, reviews, and clicks. However, most recommender systems focus mainly on the relationships between items and the user’s final purchasing behavior while ignoring the user’s emotional changes, which play an essential role in consumption activity. To address the challenge of improving the quality of recommender services, this paper proposes an emotion-aware recommender system based on hybrid information fusion in which three representative types of information are fused to comprehensively analyze the user’s features: user rating data as explicit information, user social network data as implicit information and sentiment from user reviews as emotional information. The experimental results verify that the proposed approach provides a higher prediction rating and significantly increases the recommendation accuracy.     

Learning disentangled user representation with multi-view information fusion on social networks

User representation learning is one prominent and critical task of user analysis on social networks, which derives conceptual user representations to improve the inference of user intentions and behaviors. Previous efforts have shown its substantial value in multifarious real-world applications, including product recommendation, textual content modeling, link prediction, and many more. However, existing studies either underutilize multi-view information, or neglect the stringent entanglement among underlying factors that govern user intentions, thus deriving deteriorated representations. To overcome these shortages, this paper proposes an adversarial fusion framework to fully exploit substantial multi-view information for user representation, consisting of a generator and a discriminator. The generator learns representations with a variational autoencoder, and is forced by the adversarial fusion framework to pay specific attention to substantial informative signs, thus integrating multi-view information. Furthermore, the variational autoencoder used in the generator is novelly designed to capture and disentangle the latent factors behind user intentions. By fully utilizing multi-view information and achieving disentanglement, our model learns robust and interpretable user representations. Extensive experiments on both synthetic and real-world datasets demonstrate the superiority of our proposed model.