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Recommending purely cold-start items is a long-standing and fundamental challenge in the recommender systems. Without any historical interaction on cold-start items, the collaborative filtering (CF) scheme fails to leverage collaborative signals to infer user preference on these items. To solve this problem, extensive studies have been conducted to incorporate side information of items (e.g., content features) into the CF scheme. Specifically, they employ modern neural network techniques (e.g., dropout, consistency constraint) to discover and exploit the coalition effect of content features and collaborative representations. However, we argue that these works less explore the mutual dependencies between content features and collaborative representations and lack sufficient theoretical supports, thus resulting in unsatisfactory performance on cold-start recommendation.In this work, we reformulate the cold-start item representation learning from an information-theoretic standpoint. It aims to maximize the mutual dependencies between item content and collaborative signals. Specifically, the representation learning is theoretically lower-bounded by the integration of two terms: mutual information between collaborative embeddings of users and items, and mutual information between collaborative embeddings and feature representations of items. To model such a learning process, we devise a new objective function founded upon contrastive learning and develop a simple yet efficient Contrastive Learning-based Cold-start Recommendation framework (CLCRec). In particular, CLCRec consists of three components: contrastive pair organization, contrastive embedding, and contrastive optimization modules.

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Given a collection of geo-located point samples of types, we aim to detect spatial mixture patterns of interest, which are sub-regions of the study area that have significantly high or low mixture of points of different types. Spatial mixture patterns have important applications in many societal domains, including resilience of smart cities and communities, biodiversity, equity, business intelligence, etc. The problem is challenging because ranking and selection of candidate patterns can be highly susceptible to the effect of natural randomness, and real-world data often consists of various mixture patterns. In related work, the multi-nomial scan statistic does not support identification of high or low mixture due to its "directionless" nature and high sensitivity to the composition of mixture patterns in data. While species richness indices in biodiversity research allow specification of directions, the measures are very sensitive to spatial randomness effects. To bridge the gap, we first propose a spatial mixture index to provide robust ranking among candidate patterns. Then, we present a dual-level Monte-Carlo estimation method with a baseline algorithm for spatial mixture pattern detection. Finally, we propose both an exact algorithm and a distribution-inspired sequence-reduction heuristic to accelerate the baseline approach. Experiment results with both synthetic and realworld data show that the proposed approaches can detect mixture patterns with high accuracy, and the acceleration methods can greatly reduce computational cost while maintaining high solution quality. CCS CONCEPTS • Information systems → Data mining; Spatial-temporal systems.

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