Albeit the implicit feedback based recommendation problem - when only the
user history is available but there are no ratings - is the most typical
setting in real-world applications, it is much less researched than the
explicit feedback case. State-of-the-art algorithms that are efficient on the
explicit case cannot be automatically transformed to the implicit case if
scalability should be maintained. There are few implicit feedback benchmark
data sets, therefore new ideas are usually experimented on explicit benchmarks.
In this paper, we propose a generic context-aware implicit feedback recommender
algorithm, coined iTALS. iTALS applies a fast, ALS-based tensor factorization
learning method that scales linearly with the number of non-zero elements in
the tensor. We also present two approximate and faster variants of iTALS using
coordinate descent and conjugate gradient methods at learning. The method also
allows us to incorporate various contextual information into the model while
maintaining its computational efficiency. We present two context-aware variants
of iTALS incorporating seasonality and item purchase sequentiality into the
model to distinguish user behavior at different time intervals, and product
types with different repetitiveness. Experiments run on six data sets shows
that iTALS clearly outperforms context-unaware models and context aware
baselines, while it is on par with factorization machines (beats 7 times out of
12 cases) both in terms of recall and MAP.

该论文提出了一种上下文感知的通用隐式反馈推荐算法，使用基于张量分解的学习方法，同时结合了季节性和购买顺序等上下文信息，显著优于无上下文和基准模型，并与因式机（在召回和 MAP 方面击败了 12 个案例中的 7 个）相当，具有良好的可扩展性。

通过可扩展的张量分解从隐含数据中实现上下文感知的推荐

Context-aware recommendations from implicit data via scalable tensor  factorization

Albeit, the implicit feedback based recommendation problem - when only the
user history is available but there are no ratings - is the most typical
setting in real-world applications, it is much less researched than the
explicit feedback case. State-of-the-art algorithms that are efficient on the
explicit case cannot be straightforwardly transformed to the implicit case if
scalability should be maintained. There are few if any implicit feedback
benchmark datasets, therefore new ideas are usually experimented on explicit
benchmarks. In this paper, we propose a generic context-aware implicit feedback
recommender algorithm, coined iTALS. iTALS apply a fast, ALS-based tensor
factorization learning method that scales linearly with the number of non-zero
elements in the tensor. The method also allows us to incorporate diverse
context information into the model while maintaining its computational
efficiency. In particular, we present two such context-aware implementation
variants of iTALS. The first incorporates seasonality and enables to
distinguish user behavior in different time intervals. The other views the user
history as sequential information and has the ability to recognize usage
pattern typical to certain group of items, e.g. to automatically tell apart
product types or categories that are typically purchased repetitively
(collectibles, grocery goods) or once (household appliances). Experiments
performed on three implicit datasets (two proprietary ones and an implicit
variant of the Netflix dataset) show that by integrating context-aware
information with our factorization framework into the state-of-the-art implicit
recommender algorithm the recommendation quality improves significantly.

本文提出了一种称为 iTALS 的通用上下文感知隐式反馈推荐算法，它应用了一种快速的 ALS 基张量分解学习方法，同时保持计算效率，实验结果显示，通过将上下文感知信息与我们的分解框架集成到最先进的隐式推荐算法中，推荐的质量得到了显着提高。