On content-based recommendation and user privacy in social-tagging systems

Recommendation systems and content filtering approaches based on annotations and ratings, essentially rely on users expressing their preferences and interests through their actions, in order to provide personalised content. This activity, in which users engage collectively has been named social tagging, and it is one of the most popular in which users engage online, and although it has opened new possibilities for application interoperability on the semantic web, it is also posing new privacy threats. It, in fact, consists of describing online or offline resources by using free-text labels (i.e. tags), therefore exposing the user profile and activity to privacy attacks. Users, as a result, may wish to adopt a privacy-enhancing strategy in order not to reveal their interests completely. Tag forgery is a privacy enhancing technology consisting of generating tags for categories or resources that do not reflect the user's actual preferences. By modifying their profile, tag forgery may have a negative impact on the quality of the recommendation system, thus protecting user privacy to a certain extent but at the expenses of utility loss. The impact of tag forgery on content-based recommendation is, therefore, investigated in a real-world application scenario where different forgery strategies are evaluated, and the consequent loss in utility is measured and compared.Peer ReviewedPostprint (author’s final draft

Content-based recommendation in social tagging systems

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in RecSys '10 Proceedings of the fourth ACM conference on Recommender systems , http://dx.doi.org/10.1145/10.1145/1864708.1864756.In a general collaborative filtering (CF) setting, a user profile contains a set of previously rated items and is used to represent the user's interest. Unfortunately, most CF approaches ignore the underlying structure of user profiles. In this paper, we argue that a certain class of interest is best represented jointly by several items, drawing an analogy to "phrases" in text retrieval, which are not equivalent to the separate meaning of their words. At an alternative stance, we also consider the situation where, analogously to word synonyms, two items might be substitutable when representing a class of interest. We propose an approach integrating these two notions as opposing poles on a continuum spectrum. Upon this, we model the underlying structure in user profiles, drawing an analogy with text retrieval. The approach gives rise to a novel structured Vector Space Model for CF. We show that item-based CF approaches are a special case of the proposed method

Graph-RAT: Combining data sources in music recommendation systems

The complexity of music recommendation systems has increased rapidly in recent years, drawing upon different sources of information: content analysis, web-mining, social tagging, etc. Unfortunately, the tools to scientifically evaluate such integrated systems are not readily available; nor are the base algorithms available. This article describes Graph-RAT (Graph-based Relational Analysis Toolkit), an open source toolkit that provides a framework for developing and evaluating novel hybrid systems. While this toolkit is designed for music recommendation, it has applications outside its discipline as well. An experiment—indicative of the sort of procedure that can be configured using the toolkit—is provided to illustrate its usefulness

Design of Front-End for Recommendation Systems: Towards a Hybrid Architecture

To provide personalized online shopping suggestions, recommendation systems play an increasingly important role in “closing a transaction”. Some leading online movie sales platforms, such as Netflix and Rotten Tomatoes, have exploited content-based recommendation approaches. However, the issue of insufficient information about features in item profiles may lead to less accurate recommendations. In this paper, we propose a recommendation method known as Collective Intelligence Social Tagging (CIST), which combines a content-based recommendation approach with a social tagging function based on crowd-sourcing. We used an online movie sales platform as a use-case of how a CIST approach could increase the accuracy of recommended results and the overall user experience. In order t0 understand the feasibility and satisfaction level for CIST, we conducted fifteen design interviews to first determine user-developer perspectives on CIST, and then collected their overall design input

Design of Back-End of Recommendation Systems Using Collective Intelligence Social Tagging

Recommendation systems are the tools whose purpose is to suggest relevant products or services to the customers. In a movie business website, the recommendation system provides users with more options, classify movies under different types to assist in arriving at a decision. Although, with current e-commerce giants focusing on hybrid filtering approach, we have decided to explore the functionality of Content-based recommendation system. This research paper aims to delve deeper into the content-based recommendation system and adding tags to enhance its functionality. The content-based approach is more fit to the movie recommendation as it overcomes the ‘cold start’ issue faced by the collaborative filtering approach, meaning, even with no ratings for a movie, it can still be recommended. The proposed method is to solve the less ‘data categorization’ issue in content-based filtering. Collective Intelligence Social Tagging System (CIST) aims at making a significant difference in content-based recommendation system to enrich the item profile and provide more accurate suggestions. The main gist of CIST is to involve the users to contribute in tagging to build a more robust system in online movie businesses. Tags in the millennial world are the ‘go to’ words that everyone looks up to in an online world of E-commerce. It’s the easiest way of telling a story without actual long sentences. We recommended three main solutions for the concerns of CIST, (a) clustering of tags to avoid synonymous tag confusion and create a metadata for movies under same tags, (b) 5 criteria model to motivate and give the most amount of genuine information for end users to trust and eventually contribute in tagging, and (c) clear way of distinguishing and displaying tags to separate primary tags and secondary tags and give a chance to the users to assess whether the given tags reflect the relevant theme of the film

Hybrid Recommender System Using Random Walk with Restart for Social Tagging System

Social Tagging Systems (STS) are very popular web application so that millions of people join the systems and actively share their contents. This enormous number of users floods STS with contents and tags in an unrestrained way in that threatening the capability of the system for relevant content retrieval and information sharing. Recommender Systems (RS) is a known successful method for information overload problem by filtering the relevant contents over the non-relevant contents. Besides managing folksonomy information, STS also handles social network information of its users. Both information can be used by RS to generate a good recommendation for its users. This work proposes an enhanced method for an existing hybrid recommender system, by incorporating social network information into the input of the hybrid recommender. The recommendation generation process includes Random Walk with Restart (RWR) alongside Content-Based Filtering (CBF) and Collaborative Filtering (CF) methods. Some parameters are introduced in the system to control weight contribution of each method. A comprehensive experiment with a set of a real-world open data set in two areas, social bookmark (Delicious.com) and music sharing (Last.fm) to test the proposed hybrid recommender system. The outcomes exhibit that this hybrid can give improvement compared to an existing method in terms of accuracy. The proposed hybrid achieves 24.4% higher than RWR on the Delicious dataset, and 53.85% higher than CBF on Lastfm dataset. By these observational tests, it can be inferred that the proposed hybrid recommender utilizing social network information owned by Social Tagging Systems can enhance the recommendation accuracy

Quest for relevant tags using local interaction networks and visual content

Typical tag recommendation systems for photos shared on social networks such as Flickr, use visual content analysis, collaborative filtering or personalization strategies to pro-duce annotations. However, the dependence on manual in-tervention and the knowledge of sufficient personal prefer-ences coupled with the folksonomic issues limit the scope of these strategies. In this paper, we present a fully au-tomatic and folksonomically scalable tag recommendation model that can recommend tags for a user’s photos without an explicit knowledge of the user’s personal tagging pref-erences. The model is learned using the collective tagging behavior of other users in the user’s local interaction net-work, which we believe approximates the user’s preferences, at least partially. The tag recommendation model gener-ates content-based annotations and then uses a Näıve Bayes formulation to translate these annotations to a set of folk-sonomic tags selected from the tags used by the users in the local interaction network. Quantitative and qualitative com-parisons with 890 Flickr networks show that this approach is highly useful for tag recommendation in the presence of insufficient information of a user’s own preferences

Content Reuse and Interest Sharing in Tagging Communities

Tagging communities represent a subclass of a broader class of user-generated content-sharing online communities. In such communities users introduce and tag content for later use. Although recent studies advocate and attempt to harness social knowledge in this context by exploiting collaboration among users, little research has been done to quantify the current level of user collaboration in these communities. This paper introduces two metrics to quantify the level of collaboration: content reuse and shared interest. Using these two metrics, this paper shows that the current level of collaboration in CiteULike and Connotea is consistently low, which significantly limits the potential of harnessing the social knowledge in communities. This study also discusses implications of these findings in the context of recommendation and reputation systems.Comment: 6 pages, 6 figures, AAAI Spring Symposium on Social Information Processin

Enriching ontological user profiles with tagging history for multi-domain recommendations

Many advanced recommendation frameworks employ ontologies of various complexities to model individuals and items, providing a mechanism for the expression of user interests and the representation of item attributes. As a result, complex matching techniques can be applied to support individuals in the discovery of items according to explicit and implicit user preferences. Recently, the rapid adoption of Web2.0, and the proliferation of social networking sites, has resulted in more and more users providing an increasing amount of information about themselves that could be exploited for recommendation purposes. However, the unification of personal information with ontologies using the contemporary knowledge representation methods often associated with Web2.0 applications, such as community tagging, is a non-trivial task. In this paper, we propose a method for the unification of tags with ontologies by grounding tags to a shared representation in the form of Wordnet and Wikipedia. We incorporate individuals' tagging history into their ontological profiles by matching tags with ontology concepts. This approach is preliminary evaluated by extending an existing news recommendation system with user tagging histories harvested from popular social networking sites