7 Result(s)
within
Computer Science
Artificial Intelligence
Data Mining and Knowledge Discovery
Sihem Amer-Yahia
Chapter
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Chapter and Conference Paper
Multi-Objective Recommendations and Promotions at TOTAL
In this paper, we revisit the semantics of recommendations and promotional offers using multi-objective optimization principles. We investigate two formulations of product recommendation that go beyond traditi...
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Chapter and Conference Paper
An Efficient Greedy Algorithm for Sequence Recommendation
Recommending a sequence of items that maximizes some objective func...
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Chapter and Conference Paper
Scalable Active Constrained Clustering for Temporal Data
In this paper, we introduce a novel interactive framework to handle both instance-level and temporal smoothness constraints for clustering large temporal data. It consists of a constrained clustering algorithm...
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Chapter and Conference Paper
Online Lattice-Based Abstraction of User Groups
User data is becoming increasingly available in various domains from the social Web to location check-ins and smartphone usage traces. Due to the sparsity and impurity of user data, we propose to analyze label...
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Chapter and Conference Paper
Multi-Objective Group Discovery on the Social Web
We are interested in discovering user groups from collaborative rating datasets of the form \(\langle i, u, s\rangle \) ...
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Chapter and Conference Paper
TopPI: An Efficient Algorithm for Item-Centric Mining
We introduce TopPI, a new semantics and algorithm designed to mine long-tailed datasets. For each item, and regardless of its frequency, TopPI finds the k most frequent closed itemsets that item belongs to. For e...
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Chapter
Increasing Coverage in Distributed Search and Recommendation with Profile Diversity
With the advent of Web 2.0 users are producing bigger and bigger amounts of diverse data, which are stored in a large variety of systems. Since the users’ data spaces are scattered among those independent syst...
