On a serendipity-oriented recommender system based on folksonomy

This paper proposes a recommendation method that focuses on not only predictive accuracy but also serendipity. On many of the conventional recommendation methods, items are categorized according to their attributes (a genre, an authors, etc.) by the recommender in advance, and recommendation is made using the results of the categorization. In this study, impressions of users to items are adopted as a feature of the items, and each item is categorized according to the feature. Impressions used in such categorization are prepared using folksonomy, which classifies items using tags given by users. Next, the idea of “concepts” was introduced to avoid synonym and polysemy problems of tags. “Concepts” are impressions of users on items inferred from attached tags of folksonomy. The inferring method was also devised. A recommender system based on the method was developed in java language, and the effectiveness of the proposed method was verified through recommender experiments.     

A Review of Ontology‐Based Tag Recommendation Approaches

Tag recommender schemes suggest related tags for an untagged resource and better tag suggestions to tagged resources. Tagging is very important if the user identifies the tag that is more precise to use in searching interesting blogs. There is no clear information regarding the meaning of each tag in a tagging process. An user can use various tags for the same content, and he can also use new tags for an item in a blog. When the user selects tags, the resultant metadata may comprise homonyms and synonyms. This may cause an improper relationship among items and ineffective searches for topic information. The collaborative tag recommendation allows a set of freely selected text keywords as tags assigned by users. These tags are imprecise, irrelevant, and misleading because there is no control over the tag assignment. It does not follow any formal guidelines to assist tag generation, and tags are assigned to resources based on the knowledge of the users. This causes misspelled tags, multiple tags with the same meaning, bad word encoding, and personalized words without common meaning. This problem leads to miscategorization of items, irrelevant search results, wrong prediction, and their recommendations. Tag relevancy can be judged only by a specific user. These aspects could provide new challenges and opportunities to its tag recommendation problem. This paper reviews the challenges to meet the tag recommendation problem. A brief comparison between existing works is presented, which we can identify and point out the novel research directions. The overall performance of our ontology‐based recommender systems is favorably compared to other systems in the literature.     

Swallow: Resource and Tag Recommender System Based on Heat Diffusion Algorithm in Social Annotation Systems

Social annotation systems (SAS) allow users to annotate different online resources with keywords (tags). These systems help users in finding, organizing, and retrieving online resources to significantly provide collaborative semantic data to be potentially applied by recommender systems. Previous studies on SAS had been worked on tag recommendation. Recently, SAS‐based resource recommendation has received more attention by scholars. In the most of such systems, with respect to annotated tags, searched resources are recommended to user, and their recent behavior and click‐through is not taken into account. In the current study, to be able to design and implement a more precise recommender system, because of previous users' tagging data and users' current click‐through, it was attempted to work on the both resource (such as web pages, research papers, etc.) and tag recommendation problem. Moreover, by applying heat diffusion algorithm during the recommendation process, more diverse options would present to the user. After extracting data, such as users, tags, resources, and relations between them, the recommender system so called “Swallow” creates a graph‐based pattern from system log files. Eventually, following the active user path and observing heat conduction on the created pattern, user further goals are anticipated and recommended to him. Test results on SAS data set demonstrate that the proposed algorithm has improved the accuracy of former recommendation algorithms.     

TAFFY: incorporating tag information into a diffusion process for personalized recommendations

The last few years have witnessed an explosion of information caused by the exponential growth of the Internet and World Wide Web, which confronted us with information overload and brought about an era of big data, appealing for efficient personalized recommender systems to assist the screening of useful information from various sources. As for a recommender system with more than the fundamental object-user rating information, such accessorial information as tags can be exploited and integrated into final ranking lists to improve recommendation performance. However, although existing studies have demonstrated that tags, as the additional yet useful resource, can be designed to improve recommendation performance, most network-based approaches take users, objects and tags as two bipartite graphs, or a tripartite graph, and therefore overlook either the important information among homogeneous nodes in each sub-graph, or the bipartite relations between users, objects or tags. Moreover, recent studies have suggested that the filtration of weak relationships in networks may reasonably enhance recommendation performance of collaborative filtering methods, and it has also been demonstrated that approaches based on the diffusion processes could more effectively capture relationships between objects and users, hence exhibiting higher performance than a typical collaborative filtering method. Based on these understandings, we propose a data fusion approach that integrates historical and tag data towards personalized recommendations. Our method coverts historical and tag data into complex networks, resorts to a diffusion kernel to measure the strength of associations between users and objects, and adopts Fisher’s combined probability test to obtain the statistical significance of such associations for personalized recommendations. We validate our approach via 10-fold cross-validation experiments. Results show that our method outperforms existing methods in not only the recommendation accuracy and diversity, but also retrieval performance. We further show the robustness of our method to related parameters.     

A Novel Approach Based on Multi-View Content Analysis and Semi-Supervised Enrichment for Movie Recommendation

Although many existing movie recommender systems have investigated recommendation based on information such as clicks and tags, much less efforts have been made to explore the multimedia content of movies, which has potential information for the elicitation of the user’s visual and musical preferences.In this paper, we explore the content from three media types (image, text, audio) and propose a novel multi-view semi-supervised movie recommendation method, which represents each media type as a view space for movies.The three views of movies are integrated to predict the rating values under the multi-view framework.Furthermore, our method considers the casual users who rate limited movies.The algorithm enriches the user profile with a semi-supervised way when there are only few rating histories.Experiments indicate that the multimedia content analysis reveals the user’s profile in a more comprehensive way.Different media types can be a complement to each other for movie recommendation.And the experimental results validate that our semi-supervised method can effectively enrich the user profile for recommendation with limited rating history.     

Folksonomy‐Based Recommender Systems: A State‐of‐the‐Art Review

Collaborative tagging systems, also known as folksonomies, have grown in popularity over the Web on account of their simplicity to organize several types of content (e.g., Web pages, pictures, and video) using open‐ended tags. The rapid adoption of these systems has led to an increasing amount of users providing information about themselves and, at the same time, a growing and rich corpus of social knowledge that can be exploited by recommendation technologies. In this context, tripartite relationships between users, resources, and tags contained in folksonomies set new challenges for knowledge discovery approaches to be applied for the purposes of assisting users through recommendation systems. This review aims at providing a comprehensive overview of the literature in the field of folksonomy‐based recommender systems. Current recommendation approaches stemming from fields such as user modeling, collaborative filtering, content, and link‐analysis are reviewed and discussed to provide a starting point for researchers in the field as well as explore future research lines.     

A content-based goods image recommendation system

The information of e-commerce images varies and different users may focus on different contents of the same image for different purpose. So the research on recommendation by computers is becoming more and more important. But retrieval based only on keywords obviously falls short for massive numbers of resource images. In this paper, we focus on a recommendation system of goods images based on image content. Goods images have a relatively homogenous background and have a wide range of applications. The recommendation consists of three stages. First, the image is pre-processed by removing the background. Second, a weighted representation model is proposed to represent the image. The separated features are extracted and normalized, and then the weights of each feature are computed based on the samples browsed by the users. Third, a feature indexing scheme is put forward based on the proposed representation. A binary-tree is used for the indexing, and a binary-tree updating algorithm is also given. Finally, the recommended images are given by a features combination searching scheme. Experimental results on a real goods image database show that our algorithm can achieve high accuracy in recommending similar goods images with high speed.     

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.     

一种基于子空间学习的图像标签推荐方法

Represented by Flickr and Picasa, online photo albums allow users to tag images, hoping to make it more convenient as well as efficient to organize and retrieve image resources. Recently, automatic tag recommendation system has become a hot research field considering the increasing request that high-quality tags be provided. In this thesis, a new method for tag recommendation system is proposed. Unlike the traditional one which only depends on frequency information or visual feature similarity while neglecting the relation between visual content and the semantic meaning contained in tags thus leading to unsatisfactory recommendations, the new method can find out a latent subspace shared by visual features and tag contents using matrix factorization. As for an untagged image, recommendations can be made when its visual features are projected into the latent subspace and the relevance level it has with others tags is figured out. This new method has been proved efficient after being tested on NUS-WIDE data set with more satisfactory results.     

旅游推荐系统研究综述

为用户提供个性化推荐服务并提高推荐的准确度和用户满意度,是当前旅游推荐系统的主要研究任务。文中分析了旅游推荐系统与传统推荐系统的异同点,并从基于内容的推荐、基于协同过滤的推荐、基于知识的推荐、基于人口统计的推荐、混和型推荐以及基于位置感知的推荐共6个方面考查了旅游推荐的研究现状。在此基础上,给出了旅游推荐系统的一个总体框架。最后,总结分析了旅游推荐系统面临的6个重点和难点问题,并指出了下一步需要关注的研究方向。     

一种检测兴趣漂移的图结构推荐系统

协同过滤是构造推荐系统最有效的方法之一.其中,基于图结构推荐方法成为近来协同过滤的研究热点.基于图结构的方法视用户和项为图的结点,并利用图理论去计算用户和项之间的相似度.尽管人们对图结构推荐系统开展了很多的研究和应用,然而这些研究都认为用户的兴趣是保持不变的,所以不能够根据用户兴趣的相关变化做出合理推荐.本文提出一种新的可以检测用户兴趣漂移的图结构推荐系统.首先,设计了一个新的兴趣漂移检测方法,它可以有效地检测出用户兴趣在何时发生了哪种变化.其次,根据用户的兴趣序列,对评分项进行加权并构造用户特征向量.最后,整合二部投影与随机游走进行项推荐.在标准数据集MovieLens上的测试表明算法优于两个图结构推荐方法和一个评分时间加权的协同过滤方法.     

Tag recommendation by machine learning with textual and social features

Tags are very popular in social media (like Youtube, Flickr) and provide valuable and crucial information for social media. But at the same time, there exist a great number of noisy tags, which lead to many studies on tag suggestion and recommendation for items including websites, photos, books, movies, and so on. The textual features of tags, likes tag frequency, have mostly been used in extracting tags that are related to items. In this paper, we address the problem of tag recommendation for social media users. This issue is as important as the tag recommendation for items, because the tags representing users are strongly related to the users’ favorite topics. We propose several novel features of tags for machine learning that we call social features as well as textual features. The experimental results of Flickr show that our proposed scheme achieves viable performance on tag recommendation for users.     

Trinity: Walking on a User-Object-Tag Heterogeneous Network for Personalised Recommendations

The rapid evolution of the Internet has been appealing for effective recommender systems to pinpoint useful information from online resources. Although historical rating data has been widely used as the most important information in recommendation methods, recent advancements have been demonstrating the improvement in recommendation performance with the incorporation of tag information. Furthermore, the availability of tag annotations has been well addressed by such fruitful online social tagging applications as CiteULike, MovieLens and BibSonomy, which allow users to express their preferences, upload resources and assign their own tags. Nevertheless, most existing tag-aware recommendation approaches model relationships among users, objects and tags using a tripartite graph, and hence overlook relationships within the same types of nodes. To overcome this limitation, we propose a novel approach, Trinity, to integrate historical data and tag information towards personalised recommendation. Trinity constructs a three-layered object-user-tag network that considers not only interconnections between different types of nodes but also relationships within the same types of nodes. Based on this heterogeneous network, Trinity adopts a random walk with restart model to assign the strength of associations to candidate objects, thereby providing a means of prioritizing the objects for a query user. We validate our approach via a series of large-scale 10-fold cross-validation experiments and evaluate its performance using three comprehensive criteria. Results show that our method outperforms several existing methods, including supervised random walk with restart, simulation of resource allocating processes, and traditional collaborative filtering.     

The effects of transparency on trust in and acceptance of a content-based art recommender

The increasing availability of (digital) cultural heritage artefacts offers great potential for increased access to art content, but also necessitates tools to help users deal with such abundance of information. User-adaptive art recommender systems aim to present their users with art content tailored to their interests. These systems try to adapt to the user based on feedback from the user on which artworks he or she finds interesting. Users need to be able to depend on the system to competently adapt to their feedback and find the artworks that are most interesting to them. This paper investigates the influence of transparency on user trust in and acceptance of content-based recommender systems. A between-subject experiment (N = 60) evaluated interaction with three versions of a content-based art recommender in the cultural heritage domain. This recommender system provides users with artworks that are of interest to them, based on their ratings of other artworks. Version 1 was not transparent, version 2 explained to the user why a recommendation had been made and version 3 showed a rating of how certain the system was that a recommendation would be of interest to the user. Results show that explaining to the user why a recommendation was made increased acceptance of the recommendations. Trust in the system itself was not improved by transparency. Showing how certain the system was of a recommendation did not influence trust and acceptance. A number of guidelines for design of recommender systems in the cultural heritage domain have been derived from the study’s results.

Content-based and collaborative techniques for tag recommendation: an empirical evaluation

The rapid growth of the so-called Web 2.0 has changed the surfers’ behavior. A new democratic vision emerged, in which users can actively contribute to the evolution of the Web by producing new content or enriching the existing one with user generated metadata. In this context the use of tags, keywords freely chosen by users for describing and organizing resources, spread as a model for browsing and retrieving web contents. The success of that collaborative model is justified by two factors: firstly, information is organized in a way that closely reflects the users’ mental model; secondly, the absence of a controlled vocabulary reduces the users’ learning curve and allows the use of evolving vocabularies. Since tags are handled in a purely syntactical way, annotations provided by users generate a very sparse and noisy tag space that limits the effectiveness for complex tasks. Consequently, tag recommenders, with their ability of providing users with the most suitable tags for the resources to be annotated, recently emerged as a way of speeding up the process of tag convergence. The contribution of this work is a tag recommender system implementing both a collaborative and a content-based recommendation technique. The former exploits the user and community tagging behavior for producing recommendations, while the latter exploits some heuristics to extract tags directly from the textual content of resources. Results of experiments carried out on a dataset gathered from Bibsonomy show that hybrid recommendation strategies can outperform single ones and the way of combining them matters for obtaining more accurate results.     

Integrating ontology technology with folksonomies for personalized social tag recommendation

Social tagging systems leverage social interoperability by facilitating the searching, sharing, and exchanging of tagging resources. A major drawback of existing social tagging systems is that social tags are used as keywords in keyword-based search. They focus on keywords and human interpretability rather than on computer interpretable semantic knowledge. Therefore, social tags are useful for information sharing and organizing, but they lack the computer-interpretability needed to facilitate a personalized social tag recommendation. An interesting issue is how to automatically generate a personalized social tag recommendation list to users when a resource is accessed by users. The novel solution proposed in this study is a hybrid approach based on semantic tag-based resource profile and user preference to provide personalized social tag recommendation. Experiments show that the Precision and Recall of the proposed hybrid approach effectively improves the accuracy of social tag recommendation.     

An overview of recommender systems in the healthy food domain

Recently, food recommender systems have received increasing attention due to their relevance for healthy living. Most existing studies on the food domain focus on recommendations that suggest proper food items for individual users on the basis of considering their preferences or health problems. These systems also provide functionalities to keep track of nutritional consumption as well as to persuade users to change their eating behavior in positive ways. Also, group recommendation functionalities are very useful in the food domain, especially when a group of users wants to have a dinner together at home or have a birthday party in a restaurant. Such scenarios create many challenges for food recommender systems since the preferences of all group members have to be taken into account in an adequate fashion. In this paper, we present an overview of recommendation techniques for individuals and groups in the healthy food domain. In addition, we analyze the existing state-of-the-art in food recommender systems and discuss research challenges related to the development of future food recommendation technologies.     

A survey of autoencoder-based recommender systems

In the past decade,recommender systems have been widely used to provide users with personalized products and services.However,most traditional recommender systems are still facing a challenge in dealing with the huge volume,complexity,and dynamics of information.To tackle this challenge,many studies have been conducted to improve recommender system by integrating deep learning techniques.As an unsupervised deep learning method,autoencoder has been widely used for its excellent performance in data dimensionality reduction,feature extraction,and data reconstruction.Meanwhile,recent researches have shown the high efficiency of autoencoder in information retrieval and recommendation tasks.Applying autoencoder on recommender systems would improve the quality of recommendations due to its better understanding of users,demands and characteristics of items.This paper reviews the recent researches on autoencoder-based recommender systems.The differences between autoencoder-based recommender systems and traditional recommender systems are presented in this paper.At last,some potential research directions of autoencoder-based recommender systems are discussed.     

Evaluation and selection of group recommendation strategies for collaborative searching of learning objects

Nowadays, there is a wide variety of e-learning repositories that provide digital resources for education in the form of learning objects. Some of these systems provide recommender systems in order to help users in the search for and selection of the learning objects most appropriate to their individual needs. The search for and recommendation of learning objects are usually viewed as a solitary and individual task. However, a collaborative search can be more effective than an individual search in some situations – for example, when developing a digital course between a group of instructors. The problem of recommending learning objects to a group of users or instructors is much more difficult than the traditional problem of recommending to only one individual. To resolve this problem, this paper proposes a collaborative methodology for searching, selecting, rating and recommending learning objects. Additionally, voting aggregation strategies and meta-learning techniques are used in order to automatically obtain the final ratings without having to reach a consensus between all the instructors. A functional model has been implemented within the DELPHOS hybrid recommender system. Finally, various experiments have been carried out using 50 different groups in order to validate the proposed learning object group recommendation approach.     

Social tagging in recommender systems: a survey of the state-of-the-art and possible extensions

Social tagging systems have grown in popularity over the Web in the last years on account of their simplicity to categorize and retrieve content using open-ended tags. The increasing number of users providing information about themselves through social tagging activities caused the emergence of tag-based profiling approaches, which assume that users expose their preferences for certain contents through tag assignments. Thus, the tagging information can be used to make recommendations. This paper presents an overview of the field of social tagging systems which can be used for extending the capabilities of recommender systems. Various limitations of the current generation of social tagging systems and possible extensions that can provide better recommendation capabilities are also considered.