Abstract. Clustering Transactions in sequence, temporal and time series databases is achieving an important attention from the database researchers and software industry. Significant research is carried out towards defining and validating the suitability of new similarity measures for sequence, temporal, time series databases which can accurately and efficiently find the similarity between user...

Community detection on social media is a classic and challenging task. In this paper, we study the problem of detecting communities by combining social relations and user generated content in social networks. We propose a nonnegative matrix tri-factorization (NMTF) based clustering framework with three types of graph regularization. The NMTF based clustering framework can combine the relations ...

This article reports on a modification of the user-kNN algorithm that measures the similarity between users based on the similarity of text reviews, instead of ratings. We investigate the performance of text semantic similarity measures and we evaluate our text-based user-kNN approach by comparing it to a range of ratings-based approaches in a ratings prediction task. We do so by using datasets...

Large music collections require new ways to let users interact with their music. The concept of finding ‘similar’ songs, albums, or artists provides handles to users for easy navigation and instant retrieval. This paper presents the realization and user evaluation of a music retrieval music that sorts songs on the basis of similarity to a given seed song. Similarity is based on a userweighted c...

Content-based multimedia retrieval requires an appropriate similarity model which reflects user preferences. When these preferences are unknown or when the structure of the data collection is unclear, retrieving the most preferable objects the user has in mind is challenging, as the notion of similarity varies from data to data, from task to task, and ultimately from user to user. Based on a sp...

Finding similar user pairs is a fundamental task in social networks, with numerous applications in ranking and personalization tasks such as link prediction and tie strength detection. A common manifestation of user similarity is based upon network structure: each user is represented by a vector that represents the user’s network connections, where pairwise cosine similarity among these vectors...

Sistem rekomendasi produk merupakan sebuah sistem yang dapat memberikan prediksi relevan terhadap perilaku atau karakteristik user, sehingga mempengaruhi user dalam mengambil keputusan untuk membeli suatu produk. Penelitian ini dilakukan kepada pembeli pada aplikasi marketplace Sindomall dengan menggunakan metode User Based Collaborative Filtering dikolaborasikan algoritma Improved Triangle Sim...

This paper presents an approach of recommending a ranked list of books to a user. A user profile is defined by a few liked and disliked books. To recommend a book, we calculate semantic relatedness of the given book to the liked and disliked books by using Wikipedia. Based on the obtained scores, we predict ratings of the book. We evaluate our approach on a dataset that consists of 6,181 users,...

This paper presents an implementation of a simple playlist generator. An audio-based music similarity measure and simple heuristics are used to create playlists given minimum user input. The ultimate goal or this work is to conduct a field study, i.e., to run the system on the users’ personal collection and study the usage behavior over a period of time. The functions include, for example, allo...

Requiring only category names as user input is a highly attractive, yet hardly explored, setting for text categorization. Earlier bootstrapping results relied on similarity in LSA space, which captures rather coarse contextual similarity. We suggest improving this scheme by identifying concrete references to the category name’s meaning, obtaining a special variant of lexical expansion.