Question: What is the dynamic meaning?
Evidence: Learning semantic representations (embeddings) of different entities, such as textual, commercial, and physical, has been a recent and active area of research. Such representations can facilitate applications that rely on a notion of similarity, for example recommendation systems and ranking algorithms in e-commerce.
In natural language processing, word2vec BIBREF0 learns vector representations of words from large quantities of text, where each word is mapped to a $d$-dimensional vector such that semantically similar words have geometrically closer vectors. This is achieved by predicting either the context words appearing in a window around a given target word (skip-gram model), or the target word given the context (CBOW model). The main assumption is that words appearing frequently in similar contexts share statistical properties (the distributional hypothesis). Crucially, word2vec models, like many other word embedding models, preserve sequential information encoded in text so as to leverage word co-occurrence statistics. The skip-gram model has been adapted to other domains in order to learn dense representations of items other than words. For example, product embeddings in e-commerce BIBREF1 or vacation rental embeddings in the hospitality domain BIBREF2 can be learned by treating purchase histories or user click sequences as sentences and applying a word2vec approach.
Most of the prior work on item embedding exploit the co-occurrence of items in a sequence as the main signal for learning the representation. One disadvantage of this approach is that it fails to incorporate rich structured information associated with the embedded items. For example, in the travel domain, where we seek to embed hotels and other travel-related entities, it could be helpful to encode explicit information such as user ratings, star ratings, hotel amenities, and location in addition to implicit information encoded in the click-stream.
In this work, we propose an algorithm for learning hotel embeddings that combines sequential user click information in a word2vec approach with additional structured information about hotels. We propose a neural architecture that adopts and extends the skip-gram model to accommodate arbitrary relevant information of embedded items, including but not limited to geographic information, ratings, and item attributes. In experimental results, we show that enhancing the neural network to jointly encode click and supplemental structured information outperforms a skip-gram model that encodes the click information alone. The proposed architecture also naturally handles the cold-start problem for hotels with little or no historical clicks. Specifically, we can infer an embedding for these properties by leveraging their supplemental structured metadata.
Compared to previous work on item embeddings, the novel contributions of this paper are as follows:
We propose a novel framework for fusing multiple sources of information about an item (such as user click sequences and item-specific information) to learn item embeddings via self-supervised learning.
We generate an interpretable embedding which can be decomposed into sub-embeddings for clicks, location, ratings, and attributes, and employed either as separate component embeddings or a single, unified embedding.
It is also dynamic, meaning it is easy to reflect future changes in attributes such as star-rating or addition of amenities in the embedding vectors without retraining.
We address the cold-start problem by including hotel metadata which are independent of user click-stream interactions and available for all hotels. This helps us to better impute embeddings for sparse items/hotels.
We show significant gains over previous work based on click-embedding in several experimental studies.
The structure of the remainder of this paper is as follows. Section 2 gives an overview of some of the recent works on neural embedding. Section 3 provides details of the proposed framework, including the neural network architecture, training methodology, and how the cold-start problem is addressed. In Section 4, we present experimental results on several different tasks and a comparison with previous state-of-the-art work. Section 5 concludes the paper.
It is also dynamic, meaning it is easy to reflect future changes in attributes such as star-rating or addition of amenities in the embedding vectors without retraining.
Answer: It is also dynamic, meaning it is easy to reflect future changes in attributes such as star-rating or addition of amenities in the embedding vectors without retraining.