eCommerce Bot

The eCommerce bot intends to retrieve product items from catalog in sorted order according to the BLEU measure [1]. In addition, it asks an user to provide additional information to specify the search (as on the example below). The order of attributes is based on information gain [2].

Here is a simple example of interaction:

>>      Hello, I am a new eCommerce bot.
        I will help you to find products that you are looking for.
        Please type your query in plain text.

x::bluetooth speaker
>>      This is what I found for you:

        - Bluetooth Speaker (Black & Red)
        - Bose SoundLink Bluetooth Speaker III
        - Bose SoundLink Mini Bluetooth Speaker
        - Bose SoundLink Mini Bluetooth Speaker
        - JBL Flip Wireless Bluetooth Speaker (Black)

        To specify the search, please choose a Brand: JBL, Soundsworks

>>      The following items satisfy your request

        - JBL Flip Wireless Bluetooth Speaker (Black)
        - JBL Flip Wireless Bluetooth Speaker (Black)
        - JBL Charge Portable Indoor/Outdoor Bluetooth Speaker | Black



  • Pretrained named entity recognition model (NER)
  • Pretrained part of speech tagger (POS)

Usage example

To interact with a pretrained model run:

python -m deeppavlov interact <path_to_config> [-d]

where <path_to_config> is a path to config file.

You can also train your own model by specifying config file and running:

python -m deeppavlov train <path_to_config>

Configuration settings

The eCommerce bot configuration consists of the following parts:

  • dataset_reader
  • dataset_iterator
  • chainer

You can use your own dataset_reader, dataset_iterator for specific data.

Let’s consider chainer in more details.


  • chainer - pipeline manager

    • in - pipeline input data: an user query and a dialog state.
    • out - pipeline output data: response the structure with retrieved product items.
  • ecommerce_bot - BLEU-based textual similarity ranker.

    • min_similarity: lower boundary for textual similarity ranker (by default 0.5).
    • min_entropy: lower boundary for entropy (by default 0.5). If the entropy is less than min_entropy, it’s omitted from the specification list.
    • entropy_fields: the specification attributes of the catalog items (by default “Size”, “Brand”, “Author”, “Color”, “Genre”).
    • preprocess: text preprocessing component.


    • query: a plain text user query.
    • state: dialog state.


    • items: product items in sorted order from start index till end index (taken from the dialog state).
    • entropies: specification attributes with corresponding values in sorted order.
    • total: total number of retrieved results.
    • confidence: similarity confidence.
    • state: dialog state.


[1] Papineni, Kishore, et al. “BLEU: a method for automatic evaluation of machine translation.” Proceedings of the 40th annual meeting on association for computational linguistics. Association for Computational Linguistics, 2002.