Key Algorithms for ELK-Based Search Engines for Digital E-Commerce Platforms

Search Algorithm Word Cloud

There are several algorithms that can be used to improve the search performance of an ELK-based search engine for an e-commerce platform like Amazon. Some of the best algorithms include:

1. BM-25: BM-25 is a model for ranking search results and it is typically used in information retrieval systems. It is based on the tf-idf weighting scheme and is known for its effectiveness in ranking documents based on their relevance to a query. It is a probabilistic model that uses a bag-of-words representation of the text and a query-document similarity measure based on the frequency of terms in the text and the query.
2. TF-IDF: TF-IDF stands for “term frequency-inverse document frequency.” It is a way of measuring the importance of a word in a document based on the number of times it appears in the document and the number of documents in which it appears. It is a statistical measure that weighs the importance of each word in a document. The idea behind it is that words that occur more frequently in a document should be given more weight than words that occur less frequently. This algorithm is commonly used in text mining and information retrieval.
3. Elasticsearch Query DSL: Elasticsearch Query DSL, also known as the Domain Specific Language, is a query language used to interact with Elasticsearch, a powerful and flexible search engine and data analytics platform. The Query DSL is a JSON-based syntax that allows developers to express their search queries in a structured and intuitive way. It supports a wide range of query types, including match, term, range, and nested queries, as well as various filter and aggregation options. The Query DSL is powerful and flexible enough to handle complex search scenarios and can be used to build advanced search functionality into applications. It is an important part of the Elasticsearch ecosystem, and is used by developers to interact with and retrieve data from Elasticsearch clusters. Internally, Elasticsearch uses a query parser to convert the JSON-based query DSL into a series of lower-level queries, such as term queries, range queries, and boolean queries. These lower-level queries are then executed against the index to retrieve the relevant documents. The query parser also applies various optimizations to the query, such as predicate pushdown and query rewrites, in order to improve performance and reduce the number of documents that need to be scanned. In addition to the query parser, Elasticsearch also uses a variety of other components to execute queries, such as the index shard, which stores and retrieves the data, the query cache, which stores frequently-used queries, and the search engine, which orders the results based on the relevance score.
4. Lucene’s MoreLikeThis Query: Lucene’s MoreLikeThis Query is a query that can be used to find documents that are similar to a given document. It works by analyzing the text of the given document and using this analysis to find other documents that have similar text. The MoreLikeThis Query is based on the Lucene search library, which is the underlying technology used by Elasticsearch. The query takes several parameters, such as the fields to analyze, the number of similar documents to return, and the minimum term frequency, which controls how often a term must appear in the text to be considered relevant. MoreLikeThis queries are useful for a number of use cases, such as recommending similar products, documents, or articles to users. The query then returns the most similar documents based on the calculated similarity score. The query uses the term frequency-inverse document frequency (TF-IDF) algorithm to calculate the similarity score between the specified document and other documents in the index. This algorithm takes into account the frequency of terms in the specified document and the number of documents in the index that contain those terms. The more frequent a term is in the specified document and the less frequent it is in other documents, the higher the similarity score will be for those documents. The query also allows for several parameters to be set, such as the number of similar documents to return, the fields to analyze, and the minimum similarity score required for a document to be returned. Internally, the MoreLikeThis query uses a BooleanQuery to combine the results of multiple TermQuery, which is a query that matches documents that contain a specific term. These TermQueries are generated based on the terms found in the specified document, and their scores are combined to produce the final similarity score.
5. Faceted Search: Faceted search allows users to filter and refine search results based on various criteria such as category, price, brand, etc. Faceted search, also known as faceted navigation or faceted browsing, is a technique used in information retrieval and database systems to allow users to filter and refine their search results based on various attributes or facets of the data. These facets can be anything from categories, dates, or geographical locations, to product features, prices, or authors. The faceted search interface typically presents a set of filters or facets as a list of checkboxes, dropdown menus, or sliders that users can interact with to narrow down their search results. As users select different facets, the search engine dynamically updates the results based on the selected filters and queries the underlying database for the updated results. Faceted search is particularly useful for e-commerce websites, news websites, and library catalogs, as it allows users to quickly find what they’re looking for by narrowing down the search results using various criteria. Internally, faceted search works by leveraging the indexing and querying capabilities of the underlying search engine, such as Lucene. The search engine will create an inverted index of the documents, which allows for fast lookups of words and phrases in the documents. The search engine will also create a separate data structure called a facet index, which stores the facets and their values. When a search is performed, the search engine will first use the inverted index to find the documents that match the query, and then use the facet index to filter the results based on the selected facets.

Below is an example of how you might perform a TF-IDF and BM-25 search using PySpark

from pyspark.ml.feature import HashingTF, IDF, Tokenizer
from pyspark.sql.functions import col
from pyspark.sql import SparkSession

# Create a SparkSession
spark = SparkSession.builder.appName("TFIDFExample").getOrCreate()

# Read in the data
data = spark.read.json("path/to/data.json")

# Tokenize the data
tokenizer = Tokenizer(inputCol="text", outputCol="words")
words_data = tokenizer.transform(data)

# Perform the TF-IDF calculation
hashing_tf = HashingTF(inputCol="words", outputCol="rawFeatures", numFeatures=20)
featurized_data = hashing_tf.transform(words_data)
idf = IDF(inputCol="rawFeatures", outputCol="features")
idfModel = idf.fit(featurized_data)
rescaled_data = idfModel.transform(featurized_data)

# Perform the BM-25 calculation
bm25 = BM25(inputCol="words", outputCol="bm25")
bm25Model = bm25.fit(words_data)
bm25_data = bm25Model.transform(words_data)

# Perform the search
query = "example search query"
query_words = tokenizer.transform(spark.createDataFrame([query], ["text"]))
query_tf = hashing_tf.transform(query_words)
query_tfidf = idfModel.transform(query_tf)
query_bm25 = bm25Model.transform(query_words)

# Find the top matches
tfidf_matches = rescaled_data.select("id", "score").join(query_tfidf.select("features"), on="features", how="inner").orderBy(col("score").desc())
bm25_matches = bm25_data.select("id", "bm25").join(query_bm25.select("bm25"), on="bm25", how="inner").orderBy(col("bm25").desc())

# Show the top matches
tfidf_matches.show()
bm25_matches.show()

Here’s a sample code snippet for applying BM-25 and TF-IDF using Elasticsearch’s Query DSL

# BM-25 query
{
    "query": {
        "function_score": {
            "query": {
                "match": {
                    "text": "query_term"
                }
            },
            "functions": [
                {
                    "script_score": {
                        "script": {
                            "source": "bm25",
                            "lang": "expression",
                            "params": {
                                "field": "text",
                                "term": "query_term"
                            }
                        }
                    }
                }
            ],
            "score_mode": "multiply"
        }
    }
}

# TF-IDF query
{
    "query": {
        "function_score": {
            "query": {
                "match": {
                    "text": "query_term"
                }
            },
            "functions": [
                {
                    "script_score": {
                        "script": {
                            "source": "tfidf",
                            "lang": "expression",
                            "params": {
                                "field": "text",
                                "term": "query_term"
                            }
                        }
                    }
                }
            ],
            "score_mode": "multiply"
        }
    }
}

Here’s an example of how you can perform a MoreLikeThis Query in Elasticsearch using the Elasticsearch Query DSL

from elasticsearch import Elasticsearch
es = Elasticsearch()

query = {
    "query": {
        "more_like_this" : {
            "fields" : ["text"],
            "like" : "example text",
            "min_term_freq" : 1,
            "max_query_terms" : 12
        }
    }
}

results = es.search(index="my_index", body=query)

Here is a sample code snippet in PySpark to perform a faceted search using PySpark SQL

from pyspark.sql import SparkSession

# Create a SparkSession
spark = SparkSession.builder.appName("Faceted Search").getOrCreate()

# Read a DataFrame from a CSV file
df = spark.read.format("csv").options(header="true", inferSchema="true").load("path/to/data.csv")

# Register the DataFrame as a temporary table
df.createOrReplaceTempView("data")

# Execute a faceted search query
result = spark.sql("SELECT color, size, COUNT(*) FROM data GROUP BY color, size")

# Show the results
result.show()

It’s important to note that depending on the specific requirements and use-case of the e-commerce platform, the best algorithm may vary. However, BM-25 and TF-IDF are popular choices as they are generally well-suited for text-based search, while Elasticsearch Query DSL and Lucene’s MoreLikeThis Query are more powerful options for more advanced search queries and filtering.

Both BM-25 and TF-IDF are useful in different situations, it depends on the specific use case and the data you are working with. BM-25 can be more effective in situations where you have a large number of documents and you want to rank them according to their relevance to a query, while TF-IDF can be more effective when you want to extract keywords from a document or find the most important words in a document.

Few more Algorithms

1. Cosine similarity is a metric that measures the similarity between two documents. Cosine similarity is calculated by taking the dot product of the term vectors for the two documents. This metric can be used to rank documents in the search results based on how similar they are to the user’s query.
2. Relevance ranking is a process of ranking documents in the search results based on how relevant they are to the user’s query. Relevance ranking can be done using a variety of factors, such as the term frequency-inverse document frequency (TF-IDF) of the terms in the document, the cosine similarity of the document to the user’s query, and the click-through rate (CTR) of the document.

Factors to consider when choosing the right algorithms for a particular e-commerce platform

• The size of the e-commerce platform: The larger the e-commerce platform, the more data there will be to index and search. This means that the algorithms used to index and search the data will need to be able to handle large amounts of data.
• The type of data stored on the e-commerce platform: The type of data stored on the e-commerce platform will affect the algorithms that can be used to index and search the data. For example, if the e-commerce platform stores images, then algorithms that are designed to index and search images will need to be used.
• The budget of the e-commerce platform: The cost of implementing and maintaining the algorithms will need to be considered when choosing the right algorithms. Some algorithms are more expensive to implement and maintain than others.

If you’re looking for a powerful and comprehensive search engine and analytics solution, then the ELK Stack is the perfect choice. Bringing data to life with ELK Search Engines can open up a whole new world of possibilities. Follow me now to stay up-to-date on the latest advances in search engine and analytics technology and unlock the potential of your data.