NDLI: Ordering innovators and laggards for product categorization and recommendation

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A spatio-temporal approach to collaborative filtering

The impact of ambiguity and redundancy on tag recommendation in folksonomies

Search shortcuts: a new approach to the recommendation of queries

Stacking recommendation engines with additional meta-features

Effective diverse and obfuscated attacks on model-based recommender systems

Rate it again: increasing recommendation accuracy by user re-rating

A case study on the effectiveness of recommendations in the mobile internet

A spatial model for collaborative filtering of comments in an online discussion forum

Workshop on recommender systems and the social web

Tutorial on using social trust for recommender systems

Recsys'09 industrial keynote: top 10 lessons learned developing deploying and operating real-world recommender systems

Pairwise preference regression for cold-start recommendation

Personalized recommendation of social software items based on social relations

Increasing engagement through early recommender intervention

A unified approach to building hybrid recommender systems

Statistical attack detection

Using a trust network to improve top-N recommendation

A partial-order based active cache for recommender systems

Enhancing diversity in Top-N recommendation

RecSys'09 workshop 3: workshop on context-aware recommender systems (CARS-2009)

Up close and personalized: a marketing view of recommendation systems

Ordering innovators and laggards for product categorization and recommendation

Latent dirichlet allocation for tag recommendation

Recommending new movies: even a few ratings are more valuable than metadata

Exploiting user similarity based on rated-item pools for improved user-based collaborative filtering

Preserving privacy in collaborative filtering through distributed aggregation of offline profiles

Learning to recommend with trust and distrust relationships

A recommender system for dynamically evolving online forums

Applying relevant set correlation clustering to multi-criteria recommender systems

Collaborative prediction and ranking with non-random missing data

Improving rating estimation in recommender systems using aggregation- and variance-based hierarchical models

TagiCoFi: tag informed collaborative filtering

Regret-based optimal recommendation sets in conversational recommender systems

Assessment of conversation co-mentions as a resource for software module recommendation

Manipulation-resistant collaborative filtering systems

Personalised and dynamic trust in social networks

A semantic framework for personalized ad recommendation based on advanced textual analysis

Matching information content with music

Acceptance issues of personality-based recommender systems

Personalized query relaxations and repairs in knowledge-based recommendation

Adaptive tradeoff explanations in conversational recommenders

Situation-aware trust management

An incentive-based architecture for social recommendations

Search the web x.0: mining and recommending web-mediated processes

Analysis of cold-start recommendations in IPTV systems

Collaborative filtering for social tagging systems: an experiment with CiteULike

Conceptual recommender system for CiteSeerX

Context-based splitting of item ratings in collaborative filtering

Critiquing recommenders for public taste products

Donation dashboard: a recommender system for donation portfolios

DynamicTV: a culture-aware recommender

Ensemble methods for improving the performance of neighborhood-based collaborative filtering

Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems

FeedbackTrust: using feedback effects in trust-based recommendation systems

FriendSensing: recommending friends using mobile phones

Generating comparative descriptions of places of interest in the tourism domain

Generating transparent, steerable recommendations from textual descriptions of items

Getting recommender systems to think outside the box

Harnessing the power of "favorites" lists for recommendation systems

How does high dimensionality affect collaborative filtering?

iTag: a personalized blog tagger

Knowledge infusion into content-based recommender systems

Learning to recommend helpful hotel reviews

Maximum margin matrix factorization for code recommendation

Measuring predictive capability in collaborative filtering

MoviExplain: a recommender system with explanations

On the limitations of browsing top-N recommender systems

Personality aware recommendations to groups

Personalized recommendation based on the personal innovator degree

Predicting social-tags for cold start book recommendations

Preference aggregation in group recommender systems for committee decision-making

Preference elicitation with subjective features

Putting recommendations on the map: visualizing clusters and relations

Rating aggregation in collaborative filtering systems

Recommendations with prerequisites

Recommender systems for the conference paper assignment problem

Recommenders' influence on buyers' decision process

Team recommendation in open innovation networks

Testing and evaluating tag recommenders in a live system

The 3A contextual ranking system: simultaneously recommending actors, assets, and group activities

Uncovering functional dependencies in MDD-compiled product catalogues

Understanding the effect of adaptive preference elicitation methods on user satisfaction of a recommender system

Using twitter to recommend real-time topical news

View-based recommender systems

Ordering innovators and laggards for product categorization and recommendation

Content Provider	ACM Digital Library
Author	Tyler, Sarah K. Zhang, Yi Zhu, Shenghuo Chi, Yun
Abstract	Different buyers exhibit different purchasing behaviors. Some rush to purchase new products while others tend to be more cautious, waiting for reviews from people they trust. In market analysis, the former group of buyers is often referred to as innovators and early adopters while the latter group is referred to as laggards. The adoption behavior is a dynamic feature of the user and varies over groups of products, e.g., innovators of literature may not be the innovators of electronics. The adoption order of users is a dynamic feature of the product, which can help to predict the future potential buyers. However, such dynamic features are usually unavailable in the description of products. In this paper, we study the user behavior of an online review website- Epinions.com. We first propose to model user adoption behaviors by creating a total ordering among users who rate the products in a given category. We develop a greedy algorithm and a Markov-chain based algorithm for computing the category total ordering. Next, we show that by using user behavior information, we can more accurately predict the category of a new product as well as predict which users will follow. Furthermore, by using the Epinion.com trust network as evidence, we demonstrate that our total ordering can group users into communities that closely resemble the trust network. Thus the adoption order can be a useful feature in recommendation systems.
Starting Page	29
Ending Page	36
Page Count	8
File Format	PDF
ISBN	9781605584355
DOI	10.1145/1639714.1639721
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-10-23
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Ranking Social networks Recommendation
Content Type	Text
Resource Type	Article

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