HAM: Hybrid Associations Model with Pooling for Sequential Recommendation

by   Bo Peng, et al.
The Ohio State University

We developed a hybrid associations model (HAM) to generate sequential recommendations using two factors: 1) users' long-term preferences and 2) sequential, both high-order and low-order association patterns in the users' most recent purchases/ratings. HAM uses simplistic pooling to represent a set of items in the associations. We compare HAM with three the most recent, state-of-the-art methods on six public benchmark datasets in three different experimental settings. Our experimental results demonstrate that HAM significantly outperforms the state of the art in all the experimental settings, with an improvement as high as 27.90


page 1

page 2

page 3

page 4


M2pht: Mixed Models with Preferences and Hybrid Transitions for Next-Basket Recommendation

Next-basket recommendation considers the problem of recommending a set o...

Prospective Preference Enhanced Mixed Attentive Model for Session-based Recommendation

Session-based recommendation aims to generate recommendations for the ne...

Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding

Top-N sequential recommendation models each user as a sequence of items ...

Cascading: Association Augmented Sequential Recommendation

Recently, recommendation according to sequential user behaviors has show...

Improving End-to-End Sequential Recommendations with Intent-aware Diversification

Sequential Recommendation (SRs) that capture users' dynamic intents by m...

Cold Item Integration in Deep Hybrid Recommenders via Tunable Stochastic Gates

A major challenge in collaborative filtering methods is how to produce r...

Killing Two Birds with One Stone: Malicious Domain Detection with High Accuracy and Coverage

Inference based techniques are one of the major approaches to analyze DN...

Please sign up or login with your details

Forgot password? Click here to reset