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

x::JBL
>>      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

Usage¶

Requirements¶

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

Config file¶

For a working config file example see https://github.com/deepmipt/DeepPavlov/blob/0.0.8/deeppavlov/configs/ecommerce_bot/ecommerce_bot.json

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¶

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.
Input:
- query: a plain text user query.
- state: dialog state.
Returns:
- 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.

References¶

[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.

[2] https://en.wikipedia.org/wiki/Information_gain_ratio.