We present a novel system that helps nonexperts find sets of similar words. The user begins by specifying one or more seed words. The system then iteratively suggests a series of candidate words, which the user can either accept or reject. Current techniques for this task typically bootstrap a classifier based on a fixed seed set. In contrast, our system involves the user throughout the labelin...

Recently label propagation recommendation receives much attention from both industrial and academic fields due to its low requirement of labeled training data and effective prediction. Previous methods propagate preferences on a user or item similarity graph for making recommendation. However, they still suffer some major problems, including data sparsity, lack of trustworthiness, cold-start pr...

Memory-based collaborative recommender system (CRS) computes the similarity between users based on their declared ratings. However, not all ratings are of the same importance to the user. The set of ratings each user weights highly differs from user to user according to his mood and taste. This is usually reflected in the user’s rating scale. Accordingly, many efforts have been done to introduc...

Spatial personalization can be defined as a novel way to fulfill user information needs when accessing spatial information services either on the web or in mobile environments. The research presented in this paper introduces a conceptual approach that models the spatial information offered to a given user into a user-centered conceptual map, and spatial proximity and similarity measures that co...

We submitted six interactive search runs for TRECVID2007, including 2 single user and 4 collaborative runs. In one single-user run (FXPAL MMA) the searchers had access to all resources including text search, text similarity, image similarity, and concept similarity search. In the other single-user submission (FXPAL MMV) the transcripts were not available and only features derived directly from ...

We present a new weighted session similarity measure to capture the browsing interests of users in web usage profiles discovered from web log data. We base our similarity measure on the reasonable assumption that when users spend longer times on pages or revisit pages in the same session, then very likely, such pages are of greater interest to the user. The proposed similarity measure combines ...

Traditional neighborhood-based collaborative filtering algorithms are widely used in recommender system field for its accuracy, interpretability and operability. In this paper, we introduce expert user model into collaborative filtering and determine authoritative expert users via expert finding methods in large corpus. We propose a method to produce predications for target user. Instead of the...

This paper presents a web based recommender system aimed at supporting a user in information filtering and product bundling. The system enables the selection of travel locations, activities and attractions, and supports the bundling of a personalized travel plan. A travel plan is composed in a mixed initiative way: the user poses queries and the recommender exploits an innovative technology tha...

In this paper we propose an asymmetric semantic similarity among instances within an ontology. We aim to define a measurement of semantic similarity that exploit as much as possible the knowledge stored in the ontology taking into account different hints hidden in the ontology definition. The proposed similarity measurement considers different existing similarities, which we have combined and e...

It is possible to acquire human motion as 3dimensional time series data by the development of sensing technology in recent years. The DP matching method is well known for recognition of the time series data such as voice. The similarity between two time series data can be measured by DP matching. However, since human motion data consists of multiple mutually-synchronized time series data, the s...