1,443 research outputs found
Movie Popularity Classification based on Inherent Movie Attributes using C4.5,PART and Correlation Coefficient
Abundance of movie data across the internet makes it an obvious candidate for
machine learning and knowledge discovery. But most researches are directed
towards bi-polar classification of movie or generation of a movie
recommendation system based on reviews given by viewers on various internet
sites. Classification of movie popularity based solely on attributes of a movie
i.e. actor, actress, director rating, language, country and budget etc. has
been less highlighted due to large number of attributes that are associated
with each movie and their differences in dimensions. In this paper, we propose
classification scheme of pre-release movie popularity based on inherent
attributes using C4.5 and PART classifier algorithm and define the relation
between attributes of post release movies using correlation coefficient.Comment: 6 page
Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users
Static recommendation methods like collaborative filtering suffer from the
inherent limitation of performing real-time personalization for cold-start
users. Online recommendation, e.g., multi-armed bandit approach, addresses this
limitation by interactively exploring user preference online and pursuing the
exploration-exploitation (EE) trade-off. However, existing bandit-based methods
model recommendation actions homogeneously. Specifically, they only consider
the items as the arms, being incapable of handling the item attributes, which
naturally provide interpretable information of user's current demands and can
effectively filter out undesired items. In this work, we consider the
conversational recommendation for cold-start users, where a system can both ask
the attributes from and recommend items to a user interactively. This important
scenario was studied in a recent work. However, it employs a hand-crafted
function to decide when to ask attributes or make recommendations. Such
separate modeling of attributes and items makes the effectiveness of the system
highly rely on the choice of the hand-crafted function, thus introducing
fragility to the system. To address this limitation, we seamlessly unify
attributes and items in the same arm space and achieve their EE trade-offs
automatically using the framework of Thompson Sampling. Our Conversational
Thompson Sampling (ConTS) model holistically solves all questions in
conversational recommendation by choosing the arm with the maximal reward to
play. Extensive experiments on three benchmark datasets show that ConTS
outperforms the state-of-the-art methods Conversational UCB (ConUCB) and
Estimation-Action-Reflection model in both metrics of success rate and average
number of conversation turns.Comment: TOIS 202
Towards Comprehensive Recommender Systems: Time-Aware UnifiedcRecommendations Based on Listwise Ranking of Implicit Cross-Network Data
The abundance of information in web applications make recommendation
essential for users as well as applications. Despite the effectiveness of
existing recommender systems, we find two major limitations that reduce their
overall performance: (1) inability to provide timely recommendations for both
new and existing users by considering the dynamic nature of user preferences,
and (2) not fully optimized for the ranking task when using implicit feedback.
Therefore, we propose a novel deep learning based unified cross-network
solution to mitigate cold-start and data sparsity issues and provide timely
recommendations for new and existing users.Furthermore, we consider the ranking
problem under implicit feedback as a classification task, and propose a generic
personalized listwise optimization criterion for implicit data to effectively
rank a list of items. We illustrate our cross-network model using Twitter
auxiliary information for recommendations on YouTube target network. Extensive
comparisons against multiple time aware and cross-network base-lines show that
the proposed solution is superior in terms of accuracy, novelty and diversity.
Furthermore, experiments conducted on the popular MovieLens dataset suggest
that the proposed listwise ranking method outperforms existing state-of-the-art
ranking techniques
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