InLinx for document classification, sharing and recommendation

Abstract

This paper proposes an hybrid recommender system, InLinx, that combine content analysis and the development of virtual clusters of students and of didactical sources providing facilities to use the huge amount of digital information according to the student's personal requirements and interests. The paper proposes novel methods for information management, with special focus on the development of new algorithms and intelligent applications for personalized information sharing, filtering and retrieval. InLinx helps the student to classify domain specific information found in the Web and saved as bookmarks, to recommend these documents to other students with similar interests and to periodically notify new potential interesting documents.

Dataset-driven research for improving recommender systems for learning

“…In the context of learning, such a dataset provides useful data for recommender systems that are targeted to recommend papers to learners or teachers or to suggest suitable peer learners on the basis of common research or learning interests. Examples of paper recommenders that have been evaluated in TEL settings are InLinx (Intelligent Links) [2], Papyres [26] and pioneering work on the application of recommender systems in TEL conducted by Tang and McCalla [31]. Although research on paper recommenders has been elaborated more extensively in the Research2.0 domain that emerged in recent years, the dataset is currently one of the few available datasets that captures a very large set of user activities.…”

Supporting collaborative hierarchical classification: Bookmarks as an example

“…In addition to website recommendation, InLinx by Bighini et al [3] facilitates the automatic classification of bookmarked websites into globally predefined categories. The basis for the classification is the user's profile and the content of the web page.…”

“…• Users that own a hierarchy of folders, optionally annotated with a user description WWW recommender systems like [3] often examine the complete content of websites as data foundation and analyze it with information retrieval techniques. Instead, the presented approach relies on information extracted from the bookmarked URL itself and manually assigned annotations (title, description).…”

“…The dimensionality of the vector space was reduced by stemming, a procedure that tries to reduce the keywords to their word stems. We used Porter Stemming, a popular stemming algorithm for the English language [3], which removes suffixes based on a set of condition/action rules that specify, for example, how to remove the plural-"s" from plural terms. Additionally, very common words or terms with little information content ("and, or", etc ...) were removed by using a stopword list 5 containing 429 common English words.…”

Automatic Concept Extraction in Semantic Summarization Process

“…Currently, Web 2.0 offers poor query possibilities apart from searching by keywords or tags. There has been a great deal of interest in the development of semantic-based systems to facilitate knowledge representation and extraction and content integration [1], [2]. Semantic-based approach to retrieving relevant material can be useful to address issues like trying to determine the type or the quality of the information suggested from a personalized environment.…”