Lot or Not: Identifying Multi-Quantity Offerings in E -Commerce (2022)

Abstract: The term lot in is defined to mean an offering that contains a collection of multiple identical items for sale. In a large online marketplace, lot offerings play an important role, allowing buyers and sellers to set price levels to optimally balance supply and demand needs. In spite of their central role, platforms often struggle to identify lot offerings, since explicit lot status identification is frequently not provided by sellers. The ability to identify lot offerings plays a key role in many fundamental tasks, from matching offerings to catalog products, through ranking search results, to providing effective pricing guidance. In this work, we seek to determine the lot status (and lot size) of each offering, in order to facilitate an improved buyer experience, while reducing the friction for sellers posting new offerings. We demonstrate experimentally the ability to accurately classify offerings as lots and predict their lot size using only the offer title, by adapting state-of-the-art natural language techniques to the lot identification problem.

## introduction the term lot has its origins in the world of live auctions, where it describes the atomic unit for sale. each such lot usually has an associated multiplicity (or lot size). in global e-commerce marketplaces, the variety of products for sale is several orders of magnitude larger than that of live auctions. in this latter setting, the atomic unit for sale is referred to as an offering or listing , and does not usually have an associated multiplicity (i.e., only one object is for sale). thus, in the e-commerce setting, the term lot (or lot offering) is redefined to describe those offerings that contain a collection of multiple identical items. that is, not every offering is a lot. we further define the term lot size as the multiplicity of identical items in the collection for sale. we adopt the definition for a lot offering given by ebay in its guidelines foot_0 to sellers: a "lot" is a group of similar or identical items that are sold together to one buyer amazon uses a similar definition for the related term multi-packs. 2 lots, or multi-packs, are distinguished from bundle offerings, which contain multiple distinct (rather than identical) items @xcite . the ability to list lot offerings provides great flexibility to sellers. one reason for this is that many products come from the manufacturer as lots (e.g., a box of pencils). another reason is that lot offerings provide the seller an additional degree of freedom (the lot size), in addition to price, to maximize marketplace value by adapting to the demand of the market for their particular product. online marketplaces strive to distinguish between lot and non-lot offerings for several reasons. the first reason is to enable discovery of lot offerings as first class citizens in the electronic marketplace. that is, a local retail entrepreneur may be looking for lots of products independent of what the actual product happens to be, seeking to gain profit by buying lots and reselling the component items individually. another important scenario is allowing price-perunit comparison in aggregate. e-commerce buyers, seeking the best value, may be willing to consider purchasing a larger quantity of a product in return for a per-unit discount. consider the offerings for protective masks depicted in figure 1 . without detecting and considering the lot size of these comparable offerings, it is difficult for a customer to recognize that some offers cost much more on a per unit basis. this work is motivated to automatically detect lot offerings, but a critical reader may ask, why not ask the sellers to explicitly designate their lot offerings (and provide an explicit lot size)? in fact, such an option does exist on many e-commerce platforms. ebay foot_2 , for example, has a standalone interface for sellers to input lot entries. unfortunately, the adoption of this feature among the seller population is quite low. while sellers often have an incentive to clearly designate their offering as a lot, in practice interfaces to specify structured lot metadata are difficult to navigate. these interfaces are often unfamiliar to sellers and not standardized across marketplaces. this issue becomes more acute when sellers upload their offerings in (often large) batches, using a non-visual interface, to multiple marketplaces. rather than mark the offering as a lot explicitly, a common practice of many e-commerce sellers is to declare the lot status (and lot size) in the offering title using natural language. since the title field exists across all e-commerce platforms and is prominently displayed to potential buyers, sellers can apply this technique to convey important offering information (such as lot status and size), without needing to understand the nuance of any particular marketplace interface, as well as its own terminology and attribute definitions. in this work, we seek to determine the lot status (and lot size) of each offering, in order to facil-itate the scenarios enumerated above for buyers, while reducing the friction for sellers. although e-commerce offerings contain multiple sources of information (e.g. images, descriptions, etc.) our methods focus exclusively on the offering title. the first reason for this is the presence of powerful natural language cues for lot status and size. this is anecdotally demonstrated in our main contributions can be summarized as follows: ## related work in this work, we apply a variety of natural language processing methods to offering titles to address the lot identification task. accordingly, we review related work in two areas: research related to lots or multipacks in electronic commerce, and text representation and classification approaches relevant to our task. ## datasets recall from our discussion in the introduction, many lot offerings are not explicitly designated as lots by the seller, and thus this explicit signal cannot be relied upon. as such, we employed human agents to manually label offerings sampled at random. we collected a number of examples of lot and non-lot offering titles from across several categories on ebay, one of the world's largest online marketplaces. the label was acquired by allowing a human evaluator to look at the entire offering page, which includes the title, but also additional structured information on the offering attributes and possibly an image and description. the evaluator then provided the lot-size label for each offering under consideration. ## methods in this section, we formalize our research problem, and propose an approach for identifying lot offerings and lot size. in order to conserve space and focus the discussion, we defer the details of our novel tokenization (section a.1) , training procedure (section a.2), and model architectures (section a.3) to the appendix. ## experiments and results in our empirical evaluation, we examine the performance of the various model architectures described in section a.3 on the lot classification problem variants defined in section 4.1: binary classification and lot size prediction. to this end, we consider the following metrics: examining tables 4 and 5 , we observe that the trans_enc_sz model achieves the best performance across all datasets. this may be because for this family of tasks, the transformer encoder architecture, which only considers word ordering indirectly, is more appropriate than the recurrent encoder architecture (enc_lstm_sz), which explicitly models the word ordering. in other words, local word structure is more important than global word structure for this class of problem. furthermore, the results indicate that the pre-trained class of models (trans_enc_sz, enc_lstm_bin, enc_lstm_sz) leverage their indirect access to much larger generalpurpose datasets to achieve better performance than models that were trained "from scratch" with random weight initialization. we can also observe that modeling the binary task directly does not improve binary performance and in fact, 3 of the top 4 performers on the lot classification task are sequence models, whose output is post-processed to reach a binary decision. this indicates that the value of the additional information (the lot size) and structure used during training the sequence model outweighs the cost of additional complexity incurred by expanding the decision space. another, somewhat surprising, result is that lot size prediction accuracy for all sequence models is quite close in magnitude to the binary classification accuracy (e.g., 0.932 compared to 0.944 for the health and beauty dataset and trans_enc_sz model). thus, the models are able to predict the precise lot size correctly almost exactly as well as they are able to classify the offer as lot or not. generalizing from the results a bit, we observe that a large improvement is gained by modeling all n-grams (ngram_fc) and/or subwords (fasttext_fc) over a simple collection of heuristic features (regexp_fc). a smaller additional gain is made by using a recurrent architecture to explicitly model the temporal dynamics of the offer title (lstm_basic_sz). finally, an additional gain is achieved by introducing pre-trained high-capacity encoder architectures (enc_lstm_sz, trans_enc_sz). ## conclusions and future work in this work, we consider the task of identifying lots, e-commerce offerings that contain multiple identical items. this application has the potential to improve the online e-commerce experience for millions of users. in our experiments, we apply a number of state-of-the-art natural language processing approaches to analyze the offering titles. we show that binary sequence models, which are aimed at identifying the lot-size token within the title, are especially effective for achieving high accuracy on both lot classification and prediction tasks across multiple e-commerce domains. our models reach high performance based on title only, which is an advantage since almost all offers contain a valid title (as opposed to image, description, or key-value attributes). that said, the ability to detect lot offerings can potentially be further improved by using additional signals available for each offering beyond its title. the offer's price may also help achieve a further performance gain. the methods developed herein rely on the availability of data to perform effectively. a large amount of unlabeled domain title data is necessary to build the language model, and a smaller amount of labeled data is required to fine-tune the model to the lot identification task. in other areas, where such data is available, specifically e-commerce data in languages other than english, we conjecture that this approach can generalize well. finally, the methods developed for analyzing titles in the pursuit of lot identification are useful in other problems that arise in the curation of a large and heterogeneous e-commerce catalog, including matching offerings to products and enabling product search.