22 research outputs found
Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks
Session-based recommendations are highly relevant in many modern on-line
services (e.g. e-commerce, video streaming) and recommendation settings.
Recently, Recurrent Neural Networks have been shown to perform very well in
session-based settings. While in many session-based recommendation domains user
identifiers are hard to come by, there are also domains in which user profiles
are readily available. We propose a seamless way to personalize RNN models with
cross-session information transfer and devise a Hierarchical RNN model that
relays end evolves latent hidden states of the RNNs across user sessions.
Results on two industry datasets show large improvements over the session-only
RNNs
Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks
Recommendations can greatly benefit from good representations of the user
state at recommendation time. Recent approaches that leverage Recurrent Neural
Networks (RNNs) for session-based recommendations have shown that Deep Learning
models can provide useful user representations for recommendation. However,
current RNN modeling approaches summarize the user state by only taking into
account the sequence of items that the user has interacted with in the past,
without taking into account other essential types of context information such
as the associated types of user-item interactions, the time gaps between events
and the time of day for each interaction. To address this, we propose a new
class of Contextual Recurrent Neural Networks for Recommendation (CRNNs) that
can take into account the contextual information both in the input and output
layers and modifying the behavior of the RNN by combining the context embedding
with the item embedding and more explicitly, in the model dynamics, by
parametrizing the hidden unit transitions as a function of context information.
We compare our CRNNs approach with RNNs and non-sequential baselines and show
good improvements on the next event prediction task
Recurrent Neural Networks with Top-k Gains for Session-based Recommendations
RNNs have been shown to be excellent models for sequential data and in
particular for data that is generated by users in an session-based manner. The
use of RNNs provides impressive performance benefits over classical methods in
session-based recommendations. In this work we introduce novel ranking loss
functions tailored to RNNs in the recommendation setting. The improved
performance of these losses over alternatives, along with further tricks and
refinements described in this work, allow for an overall improvement of up to
35% in terms of MRR and Recall@20 over previous session-based RNN solutions and
up to 53% over classical collaborative filtering approaches. Unlike data
augmentation-based improvements, our method does not increase training times
significantly. We further demonstrate the performance gain of the RNN over
baselines in an online A/B test.Comment: CIKM'18, authors' versio
Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching
Personalization in marketing aims at improving the shopping experience of
customers by tailoring services to individuals. In order to achieve this,
businesses must be able to make personalized predictions regarding the next
purchase. That is, one must forecast the exact list of items that will comprise
the next purchase, i.e., the so-called market basket. Despite its relevance to
firm operations, this problem has received surprisingly little attention in
prior research, largely due to its inherent complexity. In fact,
state-of-the-art approaches are limited to intuitive decision rules for pattern
extraction. However, the simplicity of the pre-coded rules impedes performance,
since decision rules operate in an autoregressive fashion: the rules can only
make inferences from past purchases of a single customer without taking into
account the knowledge transfer that takes place between customers. In contrast,
our research overcomes the limitations of pre-set rules by contributing a novel
predictor of market baskets from sequential purchase histories: our predictions
are based on similarity matching in order to identify similar purchase habits
among the complete shopping histories of all customers. Our contributions are
as follows: (1) We propose similarity matching based on subsequential dynamic
time warping (SDTW) as a novel predictor of market baskets. Thereby, we can
effectively identify cross-customer patterns. (2) We leverage the Wasserstein
distance for measuring the similarity among embedded purchase histories. (3) We
develop a fast approximation algorithm for computing a lower bound of the
Wasserstein distance in our setting. An extensive series of computational
experiments demonstrates the effectiveness of our approach. The accuracy of
identifying the exact market baskets based on state-of-the-art decision rules
from the literature is outperformed by a factor of 4.0.Comment: Accepted for oral presentation at 25th ACM SIGKDD Conference on
Knowledge Discovery and Data Mining (KDD 2019
Off-line vs. On-line Evaluation of Recommender Systems in Small E-commerce
In this paper, we present our work towards comparing on-line and off-line
evaluation metrics in the context of small e-commerce recommender systems.
Recommending on small e-commerce enterprises is rather challenging due to the
lower volume of interactions and low user loyalty, rarely extending beyond a
single session. On the other hand, we usually have to deal with lower volumes
of objects, which are easier to discover by users through various
browsing/searching GUIs.
The main goal of this paper is to determine applicability of off-line
evaluation metrics in learning true usability of recommender systems (evaluated
on-line in A/B testing). In total 800 variants of recommending algorithms were
evaluated off-line w.r.t. 18 metrics covering rating-based, ranking-based,
novelty and diversity evaluation. The off-line results were afterwards compared
with on-line evaluation of 12 selected recommender variants and based on the
results, we tried to learn and utilize an off-line to on-line results
prediction model.
Off-line results shown a great variance in performance w.r.t. different
metrics with the Pareto front covering 68\% of the approaches. Furthermore, we
observed that on-line results are considerably affected by the novelty of
users. On-line metrics correlates positively with ranking-based metrics (AUC,
MRR, nDCG) for novice users, while too high values of diversity and novelty had
a negative impact on the on-line results for them. For users with more visited
items, however, the diversity became more important, while ranking-based
metrics relevance gradually decrease.Comment: Submitted to ACM Hypertext 2020 Conferenc
News Session-Based Recommendations using Deep Neural Networks
News recommender systems are aimed to personalize users experiences and help
them to discover relevant articles from a large and dynamic search space.
Therefore, news domain is a challenging scenario for recommendations, due to
its sparse user profiling, fast growing number of items, accelerated item's
value decay, and users preferences dynamic shift. Some promising results have
been recently achieved by the usage of Deep Learning techniques on Recommender
Systems, specially for item's feature extraction and for session-based
recommendations with Recurrent Neural Networks. In this paper, it is proposed
an instantiation of the CHAMELEON -- a Deep Learning Meta-Architecture for News
Recommender Systems. This architecture is composed of two modules, the first
responsible to learn news articles representations, based on their text and
metadata, and the second module aimed to provide session-based recommendations
using Recurrent Neural Networks. The recommendation task addressed in this work
is next-item prediction for users sessions: "what is the next most likely
article a user might read in a session?" Users sessions context is leveraged by
the architecture to provide additional information in such extreme cold-start
scenario of news recommendation. Users' behavior and item features are both
merged in an hybrid recommendation approach. A temporal offline evaluation
method is also proposed as a complementary contribution, for a more realistic
evaluation of such task, considering dynamic factors that affect global
readership interests like popularity, recency, and seasonality. Experiments
with an extensive number of session-based recommendation methods were performed
and the proposed instantiation of CHAMELEON meta-architecture obtained a
significant relative improvement in top-n accuracy and ranking metrics (10% on
Hit Rate and 13% on MRR) over the best benchmark methods.Comment: Accepted for the Third Workshop on Deep Learning for Recommender
Systems - DLRS 2018, October 02-07, 2018, Vancouver, Canada.
https://recsys.acm.org/recsys18/dlrs
Unbiased Learning for the Causal Effect of Recommendation
Increasing users' positive interactions, such as purchases or clicks, is an
important objective of recommender systems. Recommenders typically aim to
select items that users will interact with. If the recommended items are
purchased, an increase in sales is expected. However, the items could have been
purchased even without recommendation. Thus, we want to recommend items that
results in purchases caused by recommendation. This can be formulated as a
ranking problem in terms of the causal effect. Despite its importance, this
problem has not been well explored in the related research. It is challenging
because the ground truth of causal effect is unobservable, and estimating the
causal effect is prone to the bias arising from currently deployed
recommenders. This paper proposes an unbiased learning framework for the causal
effect of recommendation. Based on the inverse propensity scoring technique,
the proposed framework first constructs unbiased estimators for ranking
metrics. Then, it conducts empirical risk minimization on the estimators with
propensity capping, which reduces variance under finite training samples. Based
on the framework, we develop an unbiased learning method for the causal effect
extension of a ranking metric. We theoretically analyze the unbiasedness of the
proposed method and empirically demonstrate that the proposed method
outperforms other biased learning methods in various settings.Comment: accepted at RecSys 2020, updated several experiment
BLOB : A Probabilistic Model for Recommendation that Combines Organic and Bandit Signals
A common task for recommender systems is to build a pro le of the interests
of a user from items in their browsing history and later to recommend items to
the user from the same catalog. The users' behavior consists of two parts: the
sequence of items that they viewed without intervention (the organic part) and
the sequences of items recommended to them and their outcome (the bandit part).
In this paper, we propose Bayesian Latent Organic Bandit model (BLOB), a
probabilistic approach to combine the 'or-ganic' and 'bandit' signals in order
to improve the estimation of recommendation quality. The bandit signal is
valuable as it gives direct feedback of recommendation performance, but the
signal quality is very uneven, as it is highly concentrated on the
recommendations deemed optimal by the past version of the recom-mender system.
In contrast, the organic signal is typically strong and covers most items, but
is not always relevant to the recommendation task. In order to leverage the
organic signal to e ciently learn the bandit signal in a Bayesian model we
identify three fundamental types of distances, namely action-history,
action-action and history-history distances. We implement a scalable
approximation of the full model using variational auto-encoders and the local
re-paramerization trick. We show using extensive simulation studies that our
method out-performs or matches the value of both state-of-the-art organic-based
recommendation algorithms, and of bandit-based methods (both value and
policy-based) both in organic and bandit-rich environments.Comment: 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining,
Aug 2020, San Diego, United State
Making Neural Networks Interpretable with Attribution: Application to Implicit Signals Prediction
Explaining recommendations enables users to understand whether recommended
items are relevant to their needs and has been shown to increase their trust in
the system. More generally, if designing explainable machine learning models is
key to check the sanity and robustness of a decision process and improve their
efficiency, it however remains a challenge for complex architectures,
especially deep neural networks that are often deemed "black-box". In this
paper, we propose a novel formulation of interpretable deep neural networks for
the attribution task. Differently to popular post-hoc methods, our approach is
interpretable by design. Using masked weights, hidden features can be deeply
attributed, split into several input-restricted sub-networks and trained as a
boosted mixture of experts. Experimental results on synthetic data and
real-world recommendation tasks demonstrate that our method enables to build
models achieving close predictive performances to their non-interpretable
counterparts, while providing informative attribution interpretations.Comment: 14th ACM Conference on Recommender Systems (RecSys '20