Tag-Aware Recommender Systems: A State-of-the-art Survey

In the past decade, Social Tagging Systems have attracted increasing attention from both physical and computer science communities. Besides the underlying structure and dynamics of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent semantic relations among items, make recommendations, and so on. Specifically, this article summarizes recent progress about tag-aware recommender systems, emphasizing on the contributions from three mainstream perspectives and approaches: network-based methods, tensor-based methods, and the topic-based methods. Finally, we outline some other tag-related works and future challenges of tag-aware recommendation algorithms.Comment: 19 pages, 3 figure

A Random Walk Model for Item Recommendation in Social Tagging Systems

Social tagging, as a novel approach to information organization and discovery, has been widely adopted in many Web 2.0 applications. Tags contributed by users to annotate a variety of Web resources or items provide a new type of information that can be exploited by recommender systems. Nevertheless, the sparsity of the ternary interaction data among users, items, and tags limits the performance of tag-based recommendation algorithms. In this article, we propose to deal with the sparsity problem in social tagging by applying random walks on ternary interaction graphs to explore transitive associations between users and items. The transitive associations in this article refer to the path of the link between any two nodes whose length is greater than one. Taking advantage of these transitive associations can allow more accurate measurement of the relevance between two entities (e.g., user-item, user-user, and item-item). A PageRank-like algorithm has been developed to explore these transitive associations by spreading users\u27 preferences on an item similarity graph and spreading items\u27 influences on a user similarity graph. Empirical evaluation on three real-world datasets demonstrates that our approach can effectively alleviate the sparsity problem and improve the quality of item recommendation

Tag-Aware Recommender Systems: A State-of-the-Art Survey

In the past decade, Social Tagging Systems have attracted increasing attention from both physical and computer science communities. Besides the underlying structure and dynamics of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent semantic relations among items, make recommendations, and so on. Specifically, this article summarizes recent progress about tag-aware recommender systems, emphasizing on the contributions from three mainstream perspectives and approaches: network-based methods, tensor-based methods, and the topic-based methods. Finally, we outline some other tag-related studies and future challenges of tag-aware recommendation algorithm

An interactive human centered data science approach towards crime pattern analysis

The traditional machine learning systems lack a pathway for a human to integrate their domain knowledge into the underlying machine learning algorithms. The utilization of such systems, for domains where decisions can have serious consequences (e.g. medical decision-making and crime analysis), requires the incorporation of human experts' domain knowledge. The challenge, however, is how to effectively incorporate domain expert knowledge with machine learning algorithms to develop effective models for better decision making. In crime analysis, the key challenge is to identify plausible linkages in unstructured crime reports for the hypothesis formulation. Crime analysts painstakingly perform time-consuming searches of many different structured and unstructured databases to collate these associations without any proper visualization. To tackle these challenges and aiming towards facilitating the crime analysis, in this paper, we examine unstructured crime reports through text mining to extract plausible associations. Specifically, we present associative questioning based searching model to elicit multi-level associations among crime entities. We coupled this model with partition clustering to develop an interactive, human-assisted knowledge discovery and data mining scheme. The proposed human-centered knowledge discovery and data mining scheme for crime text mining is able to extract plausible associations between crimes, identifying crime pattern, grouping similar crimes, eliciting co-offender network and suspect list based on spatial-temporal and behavioral similarity. These similarities are quantified through calculating Cosine, Jacquard, and Euclidean distances. Additionally, each suspect is also ranked by a similarity score in the plausible suspect list. These associations are then visualized through creating a two-dimensional re-configurable crime cluster space along with a bipartite knowledge graph. This proposed scheme also inspects the grand challenge of integrating effective human interaction with the machine learning algorithms through a visualization feedback loop. It allows the analyst to feed his/her domain knowledge including choosing of similarity functions for identifying associations, dynamic feature selection for interactive clustering of crimes and assigning weights to each component of the crime pattern to rank suspects for an unsolved crime. We demonstrate the proposed scheme through a case study using the Anonymized burglary dataset. The scheme is found to facilitate human reasoning and analytic discourse for intelligence analysis

Supporting decision making process with "Ideal" software agents: what do business executives want?

According to Simon’s (1977) decision making theory, intelligence is the first and most important phase in the decision making process. With the escalation of information resources available to business executives, it is becoming imperative to explore the potential and challenges of using agent-based systems to support the intelligence phase of decision-making. This research examines UK executives’ perceptions of using agent-based support systems and the criteria for design and development of their “ideal” intelligent software agents. The study adopted an inductive approach using focus groups to generate a preliminary set of design criteria of “ideal” agents. It then followed a deductive approach using semi-structured interviews to validate and enhance the criteria. This qualitative research has generated unique insights into executives’ perceptions of the design and use of agent-based support systems. The systematic content analysis of qualitative data led to the proposal and validation of design criteria at three levels. The findings revealed the most desirable criteria for agent based support systems from the end users’ point view. The design criteria can be used not only to guide intelligent agent system design but also system evaluation

Preference Dissemination by Sharing Viewpoints: Simulating Serendipity

IC3K 2015 will be held in conjunction with IJCCI 2015International audienceThe Web currently stores two types of content. These contents include linked data from the semantic Web and user contributions from the social Web. Our aim is to represent simplified aspects of these contents within a unified topological model and to harvest the benefits of integrating both content types in order to prompt collective learning and knowledge discovery. In particular, we wish to capture the phenomenon of Serendipity (i.e., incidental learning) using a subjective knowledge representation formalism, in which several " viewpoints " are individually interpretable from a knowledge graph. We prove our own Viewpoints approach by evidencing the collective learning capacity enabled by our approach. To that effect, we build a simulation that disseminates knowledge with linked data and user contributions, similar to the way the Web is formed. Using a behavioral model configured to represent various Web navigation strategies, we seek to optimize the distribution of preference systems. Our results outline the most appropriate strategies for incidental learning, bringing us closer to understanding and modeling the processes involved in Serendipity. An implementation of the Viewpoints formalism kernel is available. The underlying Viewpoints model allows us to abstract and generalize our current proof of concept for the indexing of any type of data set

Applying Wikipedia to Interactive Information Retrieval

There are many opportunities to improve the interactivity of information retrieval systems beyond the ubiquitous search box. One idea is to use knowledge bases—e.g. controlled vocabularies, classification schemes, thesauri and ontologies—to organize, describe and navigate the information space. These resources are popular in libraries and specialist collections, but have proven too expensive and narrow to be applied to everyday webscale search. Wikipedia has the potential to bring structured knowledge into more widespread use. This online, collaboratively generated encyclopaedia is one of the largest and most consulted reference works in existence. It is broader, deeper and more agile than the knowledge bases put forward to assist retrieval in the past. Rendering this resource machine-readable is a challenging task that has captured the interest of many researchers. Many see it as a key step required to break the knowledge acquisition bottleneck that crippled previous efforts. This thesis claims that the roadblock can be sidestepped: Wikipedia can be applied effectively to open-domain information retrieval with minimal natural language processing or information extraction. The key is to focus on gathering and applying human-readable rather than machine-readable knowledge. To demonstrate this claim, the thesis tackles three separate problems: extracting knowledge from Wikipedia; connecting it to textual documents; and applying it to the retrieval process. First, we demonstrate that a large thesaurus-like structure can be obtained directly from Wikipedia, and that accurate measures of semantic relatedness can be efficiently mined from it. Second, we show that Wikipedia provides the necessary features and training data for existing data mining techniques to accurately detect and disambiguate topics when they are mentioned in plain text. Third, we provide two systems and user studies that demonstrate the utility of the Wikipedia-derived knowledge base for interactive information retrieval

RichTags: A Social Semantic Tagging System

Social tagging systems allow users associating arbitrary keywords (or tags, or labels) to resources they want to save for future recall. Such saved items are called posts or bookmarks and usually constitute shared information in social tagging systems (although access control mechanisms might be applied as well). This means that users of a social tagging system can save and share their bookmarks with each other. The term social stresses the fact that much of the usefulness of the system relies on the data the users submit and share with each other. As a member of this category of tools, RichTags aims to overcome some weaknesses of the conventional social tagging systems (folksonomies) by utilizing Semantic Web technologies. The defining characteristic of the system is that the tags constitute an ontology of meaningful concepts, which is collectively managed by the users of the system. Hence, the approach is called social semantic tagging. It overcomes the polysemy, the synonymy, and the basic level variation problems encountered in the conventional systems. As well, it offers higher precision and recall. Current realisation of semantic tagging basically concerns an effort to automatically derive semantics out of folksonomies without affecting the mechanism of tagging applied in them. In contrast, RichTags’s approach for semantic tagging is a social process relied on the collective intelligence of the users instead of automation methods. The later means that the users collectively expand the tag vocabulary throughout the tagging task, while consistency mechanisms are applied to keep the vocabulary consistent during this expansion. The basic factor that differentiates RichTags from existing proposals for the enhancement of tags with meaning is that the primary mechanism relies on human collective intelligence and not on automation methods. However, this does not mean that the proposed automation techniques could not be combined with RichTags; contrariwise they could be very useful to speed up the production of the initial set of semantic tags in the vocabulary. Finally, RichTags is not limited to enriching the tags with meaning as current efforts primarily aim to; instead it utilizes this semantic information to improve the tagging and the exploration tasks of tagging systems

Artificial Intelligence methodologies to early predict student outcome and enrich learning material

L'abstract è presente nell'allegato / the abstract is in the attachmen

Tags Are Related: Measurement of Semantic Relatedness Based on Folksonomy Network

Folksonomy and tagging systems, which allow users to interactively annotate a pool of shared resources using descriptive tags, have enjoyed phenomenal success in recent years. The concepts are organized as a map in human mind, however, the tags in folksonomy, which reflect users' collaborative cognition on information, are isolated with current approach. What we do in this paper is to estimate the semantic relatedness among tags in folksonomy: whether tags are related from semantic view, rather than isolated? We introduce different algorithms to form networks of folksonomy, connecting tags by users collaborative tagging, or by resource context. Then we perform multiple measures of semantic relatedness on folksonomy networks to investigate semantic information within them. The result shows that the connections between tags have relatively strong semantic relatedness, and the relatedness decreases dramatically as the distance between tags increases. What we find in this paper could provide useful visions in designing future folksonomy-based systems, constructing semantic web in current state of the Internet, and developing natural language processing applications