Reyn Y. Nakamoto

Reasonable tag-based collaborative filtering for social tagging systems

In this paper, we present a tag-based collaborative filtering recommendation method for use with recently popular online social tagging systems. Combining the information provided by tagging systems with the effective recommendation abilities given by collaborative filtering, we provide a website recommendation system which provides relevant, credible recommendations that match the users changing interests as well as the users bookmarking profile. Based upon user testing, our system provides a higher level of relevant recommendations over other commonly used search and recommendation methods. We describe this system as well as the relevant user testing results and its implication towards use in online social tagging systems.

Investigation of the Effectiveness of Tag-Based Contextual Collaborative Filtering in Website Recommendation

As the Internet continues to mature and become more accessible to the commonuser, the amount of available information increases exponentially. Accordingly, find-ing useful and relevant information is becoming progressively difficult. Moreover,a lot of the information available—blogs, various types of reviews, and so forth—is highly subjective and thus, hard to evaluate purely through machine algorithms.Being subjective in nature, one person may absolutely love something while the nextmay loathe the same—no single authority exists. It is in these cases where people—more so than the current ability of machine algorithms—are greatly effective inevaluating and filtering this information.For this reason, the idea of collaborative filtering (CF) was started, extensivelyresearched, and eventually deployed to relatively good amounts of success. Usingthe people and the community, recommendations of subjective information can bemade through the matching of similar users. Sites such as amazon.com [1] or movie-lens [6], etc. utilize such recommendation methods, matching users based upon theirratings and then producing recommendations. Through this, CF provides personal-ized recommendations to the users, while at the same time offering the ability to dealwith subjective material. However, the failing of CF is that it does not consider whya user likes something and what the user is interested in now. In other words, CF canrecommend relevant sites, but does not know why or when it should be appropriate.Similarly, online social tagging systems also employ the masses to evaluate anddescribe information. Instead of relying purely upon machine algorithms, people

Prophetic blogger identification based on buzzword prediction ability

Purpose Identifying important users from social media has recently attracted much attention in the information and knowledge management community. Although researchers have focused on users’ knowledge levels on certain topics or influence degrees on other users in social networks, previous works have not studied users’ prediction ability on future popularity. This paper aims to propose a novel approach to find prophetic bloggers based on their buzzword prediction ability. Design/methodology/approach The main approach is to conduct a time-series analysis in the blogosphere considering four factors: post earliness, content similarity, entry frequency and buzzword coverage. Our method has four steps: categorizing a blogger into knowledgeable categories, identifying past buzzwords, analyzing a buzzword’s peak time content and growth period and, finally, evaluating a blogger’s prediction ability on a buzzword and on a category. Findings Experimental results on real-world blog data consisting of 150 million entries from 11 million bloggers demonstrate that the proposed approach can find prophetic bloggers and outperforms others that do not take temporal features into account. Originality/value To the best of the authors’ knowledge, our approach is the first successful attempt to identify prophetic bloggers. Finding prophetic bloggers can bring great values for two reasons. First, as prophetic bloggers tend to post creative and insightful information, analysis on their blog entries may help find future buzzword candidates. Second, communication with prophetic bloggers can help understand future trends, gain insight into early adopters’ thoughts on new technology or even foresee things that will become popular.

Analyzing Early Mentioning of Past Buzzwords for Determination of Bloggers’ Buzzword Prediction Ability

The goal of our research is to discover factors which predict which words will become buzzwords—terms representing topics that have become popular—within the blogosphere. In this paper, we propose a method which evaluates bloggers’ buzzword prediction ability by analyzing how early bloggers mentioned past buzzwords. We do so by measuring how early a buzzword is first mentioned until the buzzword’s peak in popularity. We describe this method and also report the evaluation on buzzword classification.