Tag Recommendation Methods Research Articles

Image Tagging using Modified Association Rule based on Semantic Neighbors

With the rapid development of the internet, mobiles, and social image-sharing websites, a large number of images are generated daily. The huge repository of the images poses challenges for an image retrieval system. On image-sharing social websites such as Flickr, the users can assign keywords/tags to the images which can describe the content of the images. These tags play important role in an image retrieval system. However, the user-assigned tags are highly personalized which brings many challenges for retrieval of the images. Thus, it is necessary to suggest appropriate tags to the images.
 Existing methods for tag recommendation based on nearest neighbors ignore the relationship between tags. In this paper, the method is proposed for tag recommendations for the images based on semantic neighbors using modified association rule. Given an image, the method identifies the semantic neighbors using random forest based on the weight assigned to each category. The tags associated with the semantic neighbors are used as candidate tags. The candidate tags are expanded by mining tags using modified association rules where each semantic neighbor is considered a transaction. In modified association rules, the probability of each tag is calculated using TF-IDF and confidence value.
 The experimentation is done on Flickr, NUS-WIDE, and Corel-5k datasets. The result obtained using the proposed method gives better performance as compared to the existing tag recommendation methods.

Video topic detection on Micro-Blog using Relational Topic Model

To enhance the performance of the personalized tag recommendation method, a microblog video topic discovery algorithm based on common knowledge atlas analysis was proposed. Firstly, graphical form was used for expressing latent local expression in the microblog video topic and top-k similar user discovery of users represented by user topic distribution. Then, the frequency of all the tags occurring in these users was calculated and the tags most relevant with users were recommended. Next, to mine potential topic information, enhanced cosine similarity RTM model with penalty term was used for naming the tag of topic for microblog video, which enhanced the impact of united modeling on tag generation for potential topic and might discover the relationship between global tab and topic. At last, the real experiment result showed that the recommended method was superior to typical tag recommendation algorithms of several selected microblog video topics. Meanwhile, the validity of the algorithm was verified.

Neural Graph for Personalized Tag Recommendation

Traditional personalized tag recommendation methods cannot guarantee that the coupling relationships hidden in the interactions among entities are effectively encoded in the process of learning the representations of entities, resulting in insufficient expressive capacity for characterizing the preferences or attributes of entities. In this article, we first propose a graph neural networks boosted personalized tag recommendation model, namely NGTR, which integrates the graph neural networks into the pairwise interaction tensor factorization model. Specifically, we exploit the graph neural networks to capture the coupling relationships and integrate the coupling relationships into the learning of representations of entities by transmitting and assembling the representations of neighbors along the interaction graphs. In addition, we also propose a light graph neural networks boosted personalized tag recommendation model, namely LNGTR. Different from NGTR, our proposed LNGTR model removes feature transformation and nonlinear activation components as well as adopts the weighted sum of the embeddings learned at all layers as the final embedding. Experimental results on real world datasets show that our proposed personalized tag recommendation models outperform the traditional tag recommendation methods.

TagDC: A tag recommendation method for software information sites with a combination of deep learning and collaborative filtering

Software information sites (e.g., StackOverflow, Freecode, etc.) are increasingly essential for software developers to share knowledge, communicate new techniques, and collaborate. With the rapid growth of software objects, tags are widely applied to aid developers’ various operations on software information sites. Since tags are freely and optionally selected by developers, the differences in background, expression habits, and understanding of software objects among developers may cause inconsistent or inappropriate tags. To alleviate the problems of tag synonyms and tag explosion, we propose TagDC, i.e., a composite Tag recommendation method with Deep learning and Collaborative filtering.TagDC consists of two complementary modules: the word learning enhanced CNN capsule module (TagDC-DL) and the collaborative filtering module (TagDC-CF). It can improve the understanding of software objects from different perspectives. Given a new software object, TagDC can calculate a list of the combined confidence probabilities of tags and then recommend TOP-K tags by ranking the probabilities in the list. We evaluated our TagDC on nine datasets with different scales. The experimental results show that TagDC achieves a better effectiveness against two state-of-the-art baseline methods (i.e., TagCNN and FastTagRec) with a substantial improvement.

Exploiting syntactic and neighbourhood attributes to address cold start in tag recommendation

Many state-of-the-art tag recommendation methods were designed considering that an initial set of tags is available in the target object. However, the effectiveness of these methods greatly suffer in a cold start scenario in which those initial tags are absent (although other features of the target object, such as title and description, may be present). To tackle this problem, previous work extracts candidate terms directly from the text associated with the target object or from similar/related objects, and use statistical properties of the occurrence of words, such as term frequency (TF) and inverse document frequency (IDF), to rank the candidate tags for recommendation. Yet, these properties, in isolation, may not be enough to effectively rank candidate tags, specially when they are extracted from the typically small and possibly low quality texts associated with Web 2.0 objects. In this work, we analyze various syntactic patterns (e.g., syntactic dependencies between words in a sentence) of the text associated with Web 2.0 objects that can be exploited to identify and recommend tags. We also propose new tag quality attributes based on these patterns, including them as new evidence to be exploited by state-of-the-art Learning-to-Rank (L2R) based tag recommenders. We evaluate our tag recommendation methods using real data from four Web 2.0 applications, finding that, for three out of our four datasets, the inclusion of our new proposed syntactic tag quality attributes brings improvements to two L2R-based tag recommenders with gains of up to 17% in precision. Furthermore, we find that recommendations provided by these methods can be further expanded exploiting the target object’s neighbourhood (i.e., similar objects). Our characterization and feature importance analysis results show that our syntactic attributes can indeed help discriminate relevant from non-relevant tags, being complementary to other, more traditional, tag quality attributes, particularly for datasets in which the textual features are short and / or present low quality.

FastTagRec: fast tag recommendation for software information sites

Software information sites such as StackOverflow and Freeecode enable information sharing and communication for developers around the world. To facilitate correct classification and efficient search, developers need to provide tags for their postings. However, tagging is inherently an uncoordinated process that depends not only on developers' understanding of their own postings but also on other factors, including developers' English skills and knowledge about existing postings. As a result, developers keep creating new tags even though existing tags are sufficient. The net effect is an ever increasing number of tags with severe redundancy along with more postings over time. Any algorithms based on tags become less efficient and accurate. In this paper we propose FastTagRec, an automated scalable tag recommendation method using neural network-based classification. By learning existing postings and their tags from existing information, FastTagRec is able to very accurately infer tags for new postings. We have implemented a prototype tool and carried out experiments on ten software information sites. Our results show that FastTagRec is not only more accurate but also three orders of magnitude faster than the comparable state-of-the-art tool TagMulRec. In addition to empirical evaluation, we have also conducted an user study which successfully confirms the usefulness of of our approach.

Personalized Time-Aware Tag Recommendation

Personalized tag recommender systems suggest a list of tags to a user when he or she wants to annotate an item. They utilize users’ preferences and the features of items. Tensorfactorization techniques have been widely used in tag recommendation. Given the user-item pair, although the classic PITF (Pairwise Interaction Tensor Factorization) explicitly models the pairwise interactions among users, items and tags, it overlooks users’ short-term interests and suffers from data sparsity. On the other hand, given the user-item-time triple, time-aware approaches like BLL (Base-Level Learning) utilize the time effect to capture the temporal dynamics and the most popular tags on items to handle cold start situation of new users. However, it works only on individual level and the target resource level, which cannot find users’ potential interests. In this paper, we propose an unified tag recommendation approach by considering both time awareness and personalization aspects, which extends PITF by adding weightsto user-tag interaction and item-tag interaction respectively. Compared to PITF, our proposed model can depict temporal factor by temporal weights and relieve data sparsity problem by referencing the most popular tags on items. Further, our model brings collaborative filtering (CF) to time-aware models, which can mine information from global data and help improving the ability of recommending new tags. Different from the power-form functions used in the existing time aware recommendation models, we use the Hawkes process with the exponential intensity function to improve the model’s efficiency. The experimental results show that our proposed model outperforms the state of the art tag recommendation methods in accuracy and has better ability to recommend new tags.

MFS-LDA: a multi-feature space tag recommendation model for cold start problem

PurposeTags are used to annotate resources on social media platforms. Most tag recommendation methods use popular tags, but in the case of new resources that are as yet untagged (the cold start problem), popularity-based tag recommendation methods fail to work. The purpose of this paper is to propose a novel model for tag recommendation called multi-feature space latent Dirichlet allocation (MFS-LDA) for cold start problem.Design/methodology/approachMFS-LDA is a novel latent Dirichlet allocation (LDA)-based model which exploits multiple feature spaces (title, contents, and tags) for recommending tags. Exploiting multiple feature spaces allows MFS-LDA to recommend tags even if data from a feature space is missing (the cold start problem).FindingsEvaluation of a publicly available data set consisting of around 20,000 Wikipedia articles that are tagged on a social bookmarking website shows a significant improvement over existing LDA-based tag recommendation methods.Originality/valueThe originality of MFS-LDA lies in segregation of features for removing bias toward dominant features and in synchronization of multiple feature space for tag recommendation.

Tag recommendation method in folksonomy based on user tagging status

A folksonomy consists of three basic entities, namely users, tags and resources. This kind of social tagging system is a good way to index information, facilitate searches and navigate resources. The main objective of this paper is to present a novel method to improve the quality of tag recommendation. According to the statistical analysis, we find that the total number of tags used by a user changes over time in a social tagging system. Thus, this paper introduces the concept of user tagging status, namely the growing status, the mature status and the dormant status. Then, the determining user tagging status algorithm is presented considering a user’s current tagging status to be one of the three tagging status at one point. Finally, three corresponding strategies are developed to compute the tag probability distribution based on the statistical language model in order to recommend tags most likely to be used by users. Experimental results show that the proposed method is better than the compared methods at the accuracy of tag recommendation.

A survey on tag recommendation methods

Tags (keywords freely assigned by users to describe web content) have become highly popular on Web 2.0 applications, because of the strong stimuli and easiness for users to create and describe their own content. This increase in tag popularity has led to a vast literature on tag recommendation methods. These methods aim at assisting users in the tagging process, possibly increasing the quality of the generated tags and, consequently, improving the quality of the information retrieval (IR) services that rely on tags as data sources. Regardless of the numerous and diversified previous studies on tag recommendation, to our knowledge, no previous work has summarized and organized them into a single survey article. In this article, we propose a taxonomy for tag recommendation methods, classifying them according to the target of the recommendations, their objectives, exploited data sources, and underlying techniques. Moreover, we provide a critical overview of these methods, pointing out their advantages and disadvantages. Finally, we describe the main open challenges related to the field, such as tag ambiguity, cold start, and evaluation issues.

Beyond Relevance

The design and evaluation of tag recommendation methods has historically focused on maximizing the relevance of the suggested tags for a given object, such as a movie or a song. However, relevance by itself may not be enough to guarantee recommendation usefulness. Promoting novelty and diversity in tag recommendation not only increases the chances that the user will select “some” of the recommended tags but also promotes complementary information (i.e., tags), which helps to cover multiple aspects or topics related to the target object. Previous work has addressed the tag recommendation problem by exploiting at most two of the following aspects: (1) relevance, (2) explicit topic diversity, and (3) novelty. In contrast, here we tackle these three aspects conjointly, by introducing two new tag recommendation methods that cover all three aspects of the problem at different levels. Our first method, called Random Forest with topic-related attributes , or RF t , extends a relevance-driven tag recommender based on the Random Forest ( RF ) learning-to-rank method by including new tag attributes to capture the extent to which a candidate tag is related to the topics of the target object. This solution captures topic diversity as well as novelty at the attribute level while aiming at maximizing relevance in its objective function. Our second method, called Explicit Tag Recommendation Diversifier with Novelty Promotion , or xTReND , reranks the recommendations provided by any tag recommender to jointly promote relevance, novelty, and topic diversity. We use RF t as a basic recommender applied before the reranking, thus building a solution that addresses the problem at both attribute and objective levels. Furthermore, to enable the use of our solutions on applications in which category information is unavailable, we investigate the suitability of using latent Dirichlet allocation (LDA) to automatically generate topics for objects. We evaluate all tag recommendation approaches using real data from five popular Web 2.0 applications. Our results show that RF t greatly outperforms the relevance-driven RF baseline in diversity while producing gains in relevance as well. We also find that our new xTReND reranker obtains considerable gains in both novelty and relevance when compared to that same baseline while keeping the same relevance levels. Furthermore, compared to our previous reranker method, xTReD , which does not consider novelty, xTReND is also quite effective, improving the novelty of the recommended tags while keeping similar relevance and diversity levels in most datasets and scenarios. Comparing our two new proposals, we find that xTReND considerably outperforms RF t in terms of novelty and diversity with only small losses (under 4%) in relevance. Overall, considering the trade-off among relevance, novelty, and diversity, our results demonstrate the superiority of xTReND over the baselines and the proposed alternative, RF t . Finally, the use of automatically generated latent topics as an alternative to manually labeled categories also provides significant improvements, which greatly enhances the applicability of our solutions to applications where the latter is not available.

Analysis of the Impact of a Tag Recommendation System in a Real-World Folksonomy

Collaborative tagging systems have emerged as a successful solution for annotating contributed resources to online sharing platforms, facilitating searching, browsing, and organizing their contents. To aid users in the annotation process, several tag recommendation methods have been proposed. It has been repeatedly hypothesized that these methods should contribute to improving annotation quality and reducing the cost of the annotation process. It has been also hypothesized that these methods should contribute to the consolidation of the vocabulary of collaborative tagging systems. However, to date, no empirical and quantitative result supports these hypotheses. In this work, we deeply analyze the impact of a tag recommendation system in the folksonomy of Freesound, a real-world and large-scale online sound sharing platform. Our results suggest that tag recommendation effectively increases vocabulary sharing among users of the platform. In addition, tag recommendation is shown to contribute to the convergence of the vocabulary as well as to a partial increase in the quality of annotations. However, according to our analysis, the cost of the annotation process does not seem to be effectively reduced. Our work is relevant to increase our understanding about the nature of tag recommendation systems and points to future directions for the further development of those systems and their analysis.

TagCombine: Recommending Tags to Contents in Software Information Sites

Nowadays, software engineers use a variety of online media to search and become informed of new and interesting technologies, and to learn from and help one another. We refer to these kinds of online media which help software engineers improve their performance in software development, maintenance, and test processes as software information sites. In this paper, we propose TagCombine, an automatic tag recommendation method which analyzes objects in software information sites. TagCombine has three different components: 1) multi-label ranking component which considers tag recommendation as a multi-label learning problem; 2) similarity-based ranking component which recommends tags from similar objects; 3) tag-term based ranking component which considers the relationship between different terms and tags, and recommends tags after analyzing the terms in the objects. We evaluate TagCombine on four software information sites, Ask Different, Ask Ubuntu, Freecode, and Stack Overflow. On averaging across the four projects, TagCombine achieves recall@5 and recall@10 to 0.619 8 and 0.762 5 respectively, which improves TagRec proposed by Al-Kofahi et al. by 14.56% and 10.55% respectively, and the tag recommendation method proposed by Zangerle et al. by 12.08% and 8.16% respectively.

A social tag recommendation method alleviating cold start based on probabilistic graphical model

Existing social tag recommendation methods suffer from the cold start problem of tags. To this end, a PageRank-Like tags recommendation (MRF-rank) method is proposed. MRF-rank can adjust recommending chances of tags to alleviate cold start. We detect clique-group within communities, which are considered as basic units showing usage patterns of taggings. We present an ensemble Markov random field (eMRF) model to learn the usage patterns of high quality taggings and then estimate qualities of taggings with few usage records. MRF-rank is proposed, which represents estimated qualities as the weights of tag-resource edges. By specifying a preference vector of target user and resource, MRF-rank spreads the weights among vertices, and then generates tags recommendation based on the weights rank of tags. The experimental results comparing existing methods show that MRF-rank achieves better recommendations in terms of recall and precision.

Class-based tag recommendation and user-based evaluation in online audio clip sharing

Online sharing platforms often rely on collaborative tagging systems for annotating content. In this way, users themselves annotate and describe the shared contents using textual labels, commonly called tags. These annotations typically suffer from a number of issues such as tag scarcity or ambiguous labelling. Hence, to minimise some of these issues, tag recommendation systems can be employed to suggest potentially relevant tags during the annotation process. In this work, we present a tag recommendation system and evaluate it in the context of an online platform for audio clip sharing. By exploiting domain-specific knowledge, the system we present is able to classify an audio clip among a number of predefined audio classes and to produce specific tag recommendations for the different classes. We perform an in-depth user-based evaluation of the recommendation method along with two baselines and a former version that we described in previous work. This user-based evaluation is further complemented with a prediction-based evaluation following standard information retrieval methodologies. Results show that the proposed tag recommendation method brings a statistically significant improvement over the previous method and the baselines. In addition, we report a number of findings based on the detailed analysis of user feedback provided during the evaluation process. The considered methods, when applied to real-world collaborative tagging systems, should serve the purpose of consolidating the tagging vocabulary and improving the quality of content annotations.

Personalized and object-centered tag recommendation methods for Web 2.0 applications

Several Web 2.0 applications allow users to assign keywords (or tags) to provide better organization and description of the shared content. Tag recommendation methods may assist users in this task, improving the quality of the available information and, thus, the effectiveness of various tag-based information retrieval services, such as searching, content recommendation and classification. This work addresses the tag recommendation problem from two perspectives. The first perspective, centered at the object, aims at suggesting relevant tags to a target object, jointly exploiting the following three dimensions: (i) tag co-occurrences, (ii) terms extracted from multiple textual features (e.g., title, description), and (iii) various metrics to estimate tag relevance. The second perspective, centered at both object and user, aims at performing personalized tag recommendation to a target object-user pair, exploiting, in addition to the three aforementioned dimensions, a metric that captures user interests.In particular, we propose new heuristic methods that extend state-of-the-art strategies by including new metrics that estimate how accurately a candidate tag describes the target object. We also exploit three learning-to-rank (L2R) based techniques, namely, RankSVM, Genetic Programming (GP) and Random Forest (RF), for generating ranking functions that exploit multiple metrics as attributes to estimate the relevance of a tag to a given object or object-user pair. We evaluate the proposed methods using data from four popular Web 2.0 applications, namely, Bibsonomy, LastFM, YouTube and YahooVideo. Our new heuristics for object-centered tag recommendation provide improvements in precision over the best state-of-the-art alternative of 12% on average (up to 20% in any single dataset), while our new heuristics for personalized tag recommendation produce average gains in precision of 121% over the baseline. Similar performance gains are also achieved in terms of other metrics, notably recall, Normalized Discounted Cumulative Gain (NDCG) and Mean-Reciprocal Rank (MRR). Further improvements, for both object-centered (up to 23% in precision) and personalized tag recommendation (up to 13% in precision), can also be achieved with our new L2R-based strategies, which are flexible and can be easily extended to exploit other aspects of the tag recommendation problem. Finally, we also quantify the benefits of personalized tag recommendation to provide better descriptions of the target object when compared to object-centered recommendation by focusing only on the relevance of the suggested tags to the object. We find that our best personalized method outperforms the best object-centered strategy, with average gains in precision of 10%.

Folksonomy-Based Tag Recommendation for Collaborative Tagging Systems

Collaborative tagging has emerged as a common solution for labelling and organising online digital content. However, collaborative tagging systems typically suffer from a number of issues such as tag scarcity or ambiguous labelling. As a result, the organisation and browsing of tagged content is far from being optimal. In this work the authors present a general scheme for building a folksonomy-based tag recommendation system to help users tagging online content resources. Based on this general scheme, the authorse describe eight tag recommendation methods and extensively evaluate them with data coming from two real-world large-scale datasets of tagged images and sound clips. Their results show that the proposed methods can effectively recommend relevant tags, given a set of input tags and tag co-occurrence information. Moreover, the authors show how novel strategies for selecting the appropriate number of tags to be recommended can significantly improve methods performances. Approaches such as the one presented here can be useful to obtain more comprehensive and coherent descriptions of tagged resources, thus allowing a better organisation, browsing and reuse of online content. Moreover, they can increase the value of folksonomies as reliable sources for knowledge-mining.

Combining Relation and Content Analysis for Social Tagging Recommendation

标签是Web 2.0 时代信息分类与索引的重要方式.为解决标签系统所面临的不一致性、冗余性以及完备性等问题,标签推荐通过提供备选标签的方法来提高标签的质量.为了进一步提升标签推荐的质量,提出了一种基于标签系统中对象间关系与资源内容融合分析的标签推荐方法,给出了基于LDA(latent Dirichlet allocation)的融合表示对象间关系与资源内容的标签系统生成模型TSM/Forc,提出了一种基于概率的标签推荐方法,并给出了基于吉布斯(Gibbs)抽样的参数估计方法.实验结果表明,该方法可以提供比当前主流与最新方法更加准确的推荐结果.;Tagging is one of the most important ways to categorize or indexing information at the age of Web 2.0. To handle the disadvantages of tagging systems such as inconsistentcies, redundancy and incompleteness, tag recommendation methods improve the quality of tags by providing candidate tags. In order to further improve the quality of tag recommendations, a tag recommendation method is proposed which bases on a combined analysis of the relations of objects in a tagging system and the content of resources. An LDA based generative tagging system model TSM/Forc that models object relation and resource content in a combined way is introduced, together with a probabilistic based tag recommendation method and a Gibbs sampling based model parameter estimation approach. Experiment results show that the proposed method could provide more accurate recommendations than the latest methods.