Millions of people use online e-commerce platforms to search and buy products. Identifying attributes in a query is a critical component in connecting users to relevant items. However, in many cases, the queries have multiple attributes, and some of them will be in conflict with each other. For example, the query “maroon 5 dvds” has two candidate attributes, the color “maroon” or the band “maroon 5”, where only one of the attributes can be present. In this paper, we address the problem of resolving conflicting attributes in e-commerce queries. A challenge in this problem is that knowledge bases like Wikipedia that are used to understand web queries are not focused on the e-commerce domain. E-commerce search engines, however, have access to the catalog which contains detailed information about the items and its attributes. We propose a framework that leverages catalog information to resolve conflicting attributes in e-commerce queries. Our experiments on real-world queries on e-commerce platforms demonstrate that resolving conlficting attributes by leveraging catalog information significantly improves attribute identification, and also gives out more relevant search results.
E-commerce sites are being used by millions of people to buy
products in a fast and seamless manner. Users express their buying
needs through search queries, and an accurate understanding of
the query is necessary to return relevant items [
In many cases, query understanding systems have conflicting candidate attributes for a given query. In the example query, “maroon 5 dvds” the candidate attributes are the product type “dvds” the color “maroon” and the band “maroon 5”. It is not straightforward for query understanding systems to infer whether the query is referring to the band “maroon 5” or the color “maroon”. Designing algorithms to resolve conflicting query attributes can Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).
SIGIR 2018 eCom, July 2018, Ann Arbor, Michigan, USA © 2018 Copyright held by the owner/author(s).
ACM ISBN 978-x-xxxx-xxxx-x/YY/MM. https://doi.org/10.1145/nnnnnnn.nnnnnnn help e-commerce search systems to return more relevant items and better satisfy the buying needs of users.
This task faces several challenges. First, queries are short and
contain insuficient information for systems to identify attributes.
Second, knowledge bases like Wikipedia used to supplement query
text in web search [
E-commerce search systems have access to a catalog which contains the attributes of items sold by the system. Leveraging the e-commerce catalog as a knowledge base to supplement the textual information can help to resolve conflicting query attributes. In the example query ”maroon 5 dvds”, we see from the catalog that there is a significant number of items having the product type as “dvds” and have a band attribute whereas very few of them have the color attribute. This indicates that the catalog can provide valuable information which can use to resolve conflicting query attributes. Therefore, in this paper, we propose a framework to model catalog information to better identify attributes in e-commerce queries.
Specifically, we address the following questions: How to model the catalog information to resolve conflicts in query attributes? How to evaluate the impact of the framework on on e-commerce search systems? The primary contributions of the work are • Proposing the problem of resolving conflicts in query attributes for e-commerce queries; • Proposing a framework to model catalog information to identify query attributes in e-commerce queries; and • Presenting evaluations of the utility of catalog information in identifying query attributes on real-world data.
The rest of the paper is organized as follows. In Section 2, we describe the proposed framework. In Section 3, we present evaluations of the framework for identifying the query attribute and its impact on ranking relevant items. We conclude in Section 4 along with possible future directions. 2
In this section, we present our proposed framework to identify attributes in e-commerce queries. We first describe the notations used and then define the problem statement. We then use the notations to describe various aspects of the framework.
We now present the notations used in the paper. Let q be the query and A = {a1, a2, ..., an } be the set of candidate attributes in the query. Let {A−ak } be the set of attributes of size Na without the attribute ak . The problem can be formally stated as follows: “Given the query q, attribute ak , and the attribute set {A − ak } determine whether attribute ak is present in q.”
We next present our framework to identify attribute values for a given query. We explore two sets of metrics to model catalog information to assist in query attribute identification. We next describe two sets of metrics along with mathematical formulations, one related to the presence of the attribute in the query, and the second related to the presence of attribute value in the query.
The first metric set computes p(m/q), the probability of an attribute m being present in the query q. This is formulated as (1) (2) p(m/n = x ) = p(m/q) ∝ p(m/n = x ) ∗ p(n = x /q) mn nx , where p(m/n = x ) is the probability of attribute m being present where n = x , and nx is the number of items in the catalog which have value x for attribute n. Among such items mn is the number of items having values for attribute m. For a given attribute m, we compute Eq 1 for all attributes n ∈ {A − m}, resulting in a total of Na − 1 feature values. According to Eq 1, if the query has attribute value n = x , it is more likely to have attribute m if more items in the catalog with n = x also contain attribute m. In the query “maroon 5 dvds”, very few items which have the product type “dvds” have values for color, and Eq 1 metric gives a lower value for p(color /q), the probability of color attribute being present in the query.
The second metric computes p(m = l /q), the probability of attribute m having a value l for the query q. This is formulated as p(m = l /q) ∝ p(m = l /n = x ) ∗ p(n = x /q) p(m = l /n = x ) =
loд(nx ) where the number of items in the catalog having the value x for a given attribute n is nx . Among these items, let the number of items having value l for attribute m be denoted by mn . The score is higher l if more number of items having the value x for attribute n also have value l for attribute m. We repeat this for all possible attributes n ∈ {A − m} for a given attribute n resulting in an additional Na − 1 feature values. The value set for a given attribute follow a power law distribution where few values are prominent, so we employ log smoothing to make the values linearly distributed.
We integrate the scores derived from the catalog metrics into a feature set. Our metrics are scalable and hence suited for handling large-scale trafic common in an e-commerce site. We use an out of the box classifier on the feature set to determine whether or not the given query has the attribute value.
We next evaluate our framework with the help of trafic weighted random sample of 20000 queries on Walmart.com. To compare our framework, we use the baseline Dict Lookup which identifies attributes for a query by matching overalapping phrases in the query with terms in the attribute dictionary. This baseline does not address the possible conflicts that can arise between candidate attributes. For the evaluation, we take color as the attribute that has to be predicted for a query and the product type and brand as the attributes whose values are known for the query.
We design two evaluation tasks. The first task assesses the effectiveness of the framework on identifying attributes of a given S. Ranganath et al.
1.0 1.05 +5.36% query. We employ manual labeling by expert annotators for the ground truth and use Precision, Recall, and F1 as the evaluation metrics. The second task assesses the impact of the framework on the ranking relevant items for the given query. We use the orders of the query-item pair for the ground truth and nDCG@20 as the evaluation metric. The evaluation results are illustrated in Table 1.
From the table, we can see that the framework is significantly better in identifying attributes of a given e-commerce query than the baseline across all the Precision, Recall and F1 metrics. The improvement in identifying query attributes is also reflected in showing better ranking results as shown by the lift in nDCG@20. The improvement in both the tasks demonstrates that the catalog can be efectively leveraged as a knowledge base to identify attributes for a given query in a better manner, and the ability of the metric to efectively capture the relevant catalog information.
In this paper, we address the problem of identifying attributes for queries on e-commerce sites. General purpose knowledge bases used in identifying attributes for web queries are not focused on e-commerce needs. We design a framework that leverages catalog as a knowledge base to resolve conflicts in query attributes. We evaluate the framework on the set of queries from Walmart.com and demonstrate that it significantly improves results in attribute identification and ranking relevant items for e-commerce queries. Future research directions can include leveraging query catalog interactions and query sequences to design more involved metrics for attribute identification. The utility of catalog in other query understanding tasks such as query reformulation and type-ahead is also an interesting avenue for researchers to explore.