22,955 research outputs found
Graph Contrastive Learning with Multi-Objective for Personalized Product Retrieval in Taobao Search
In e-commerce search, personalized retrieval is a crucial technique for
improving user shopping experience. Recent works in this domain have achieved
significant improvements by the representation learning paradigm, e.g.,
embedding-based retrieval (EBR) and collaborative filtering (CF). EBR methods
do not sufficiently exploit the useful collaborative signal and are difficult
to learn the representations of long-tail item well. Graph-based CF methods
improve personalization by modeling collaborative signal within the user click
graph. However, existing Graph-based methods ignore user's multiple behaviours,
such as click/purchase and the relevance constraint between user behaviours and
items.In this paper, we propose a Graph Contrastive Learning with
Multi-Objective (GCL-MO) collaborative filtering model, which solves the
problems of weak relevance and incomplete personalization in e-commerce search.
Specifically, GCL-MO builds a homogeneous graph of items and then optimizes a
multi-objective function of personalization and relevance. Moreover, we propose
a modified contrastive loss for multi-objectives graph learning, which avoids
the mutual suppression among positive samples and thus improves the
generalization and robustness of long-tail item representations. These learned
item embeddings are then used for personalized retrieval by constructing an
efficient offline-to-online inverted table. GCL-MO outperforms the online
collaborative filtering baseline in both offline/online experimental metrics
and shows a significant improvement in the online A/B testing of Taobao search
Deriving Item Features Relevance from Past User Interactions
Item-based recommender systems suggest products based on the
similarities between items computed either from past user prefer-
ences (collaborative filtering) or from item content features (content-
based filtering). Collaborative filtering has been proven to outper-
form content-based filtering in a variety of scenarios. However, in
item cold-start, collaborative filtering cannot be used directly since
past user interactions are not available for the newly added items.
Hence, content-based filtering is usually the only viable option left.
In this paper we propose a novel feature-based machine learning
model that addresses the item cold-start problem by jointly exploit-
ing item content features and past user preferences. The model
learns the relevance of each content feature from the collaborative
item similarity, hence allowing to embed collaborative knowledge
into a purely content-based algorithm. In our experiments, the
proposed approach outperforms classical content-based filtering
on an enriched version of the Netflix dataset, showing that collabo-
rative knowledge can be effectively embedded into content-based
approaches and exploited in item cold-start recommendation
Content-aware Neural Hashing for Cold-start Recommendation
Content-aware recommendation approaches are essential for providing
meaningful recommendations for \textit{new} (i.e., \textit{cold-start}) items
in a recommender system. We present a content-aware neural hashing-based
collaborative filtering approach (NeuHash-CF), which generates binary hash
codes for users and items, such that the highly efficient Hamming distance can
be used for estimating user-item relevance. NeuHash-CF is modelled as an
autoencoder architecture, consisting of two joint hashing components for
generating user and item hash codes. Inspired from semantic hashing, the item
hashing component generates a hash code directly from an item's content
information (i.e., it generates cold-start and seen item hash codes in the same
manner). This contrasts existing state-of-the-art models, which treat the two
item cases separately. The user hash codes are generated directly based on user
id, through learning a user embedding matrix. We show experimentally that
NeuHash-CF significantly outperforms state-of-the-art baselines by up to 12\%
NDCG and 13\% MRR in cold-start recommendation settings, and up to 4\% in both
NDCG and MRR in standard settings where all items are present while training.
Our approach uses 2-4x shorter hash codes, while obtaining the same or better
performance compared to the state of the art, thus consequently also enabling a
notable storage reduction.Comment: Accepted to SIGIR 202
Recommendation System for News Reader
Recommendation Systems help users to find information and make decisions where they lack the required knowledge to judge a particular product. Also, the information dataset available can be huge and recommendation systems help in filtering this data according to users‟ needs. Recommendation systems can be used in various different ways to facilitate its users with effective information sorting. For a person who loves reading, this paper presents the research and implementation of a Recommendation System for a NewsReader Application using Android Platform. The NewsReader Application proactively recommends news articles as per the reading habits of the user, recorded over a period of time and also recommends the currently trending articles. Recommendation systems and their implementations using various algorithms is the primary area of study for this project. This research paper compares and details popular recommendation algorithms viz. Content based recommendation systems, Collaborative recommendation systems etc. Moreover, it also presents a more efficient Hybrid approach that absorbs the best aspects from both the algorithms mentioned above, while trying to eliminate all the potential drawbacks observed
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