Overview of Cluster-based Diversification
Cluster-based Diversification becomes a method for introducing diversity into a recommendation system using clustering of items. The basic steps of Cluster-based Diversification are as follows:
1. clustering of items: first, items with similar characteristics are grouped into the same cluster. This ensures that items with high similarity belong to the same cluster.
2. selecting items from each cluster: select one item from each cluster. This builds a sequence of items with diversity from different clusters.
3. sequence construction: the selected items are presented as a sequence. This provides a diversity of recommendation results.
The main features of Cluster-based Diversification include
Ensuring diversity: by grouping similar items into the same cluster, multiple items from the same cluster are prevented and items from different clusters are selected, thus ensuring diversity.
Efficient computation: clustering reduces the computational cost of grouping similar items and can be applied efficiently, especially for large item sets.
Flexibility: different levels of diversity can be achieved by adjusting the clustering method, the number of clusters and the number of items to be selected.
Independent of the characteristics of the target data: general clustering methods can be applied, independent of the specific data set.
Cluster-based Diversification is one of the most effective methods for introducing diversity into a recommendation system, and it is important to select appropriate clustering methods and parameters according to the data characteristics and task requirements.
Algorithms associated with Cluster-based Diversification.
Algorithms related to Cluster-based Diversification include the following methods.
1. Cluster-based Diverse Subset Selection Algorithm (CDSS): the CDSS becomes an algorithm for selecting subsets with cluster-based diversity. The basic steps are as follows.
- Clustering: the items are divided into clusters based on their similarity. This ensures that items with high similarity are grouped into the same cluster.
- Item selection per cluster: select one item from each cluster. In this case, the selected items are excluded from the other clusters.
- Construction of the final subset: the selected items are collected to construct the final diverse subset.
In this algorithm, diversity is ensured in the selection of items per cluster by selecting one item from each cluster. The specific clustering method and selection criteria depend on the problem and data.
2.Cluster-based Greedy Algorithm: This method applies the Greedy Algorithm to cluster-based diversity. The basic steps are as follows.
- Clustering: the items are divided into clusters based on their similarity.
- Initialisation: initialise an empty subset.
- Iterative item selection: select one item from each cluster and add it to the subset. Selected items are excluded from other clusters.
- Check termination condition: terminates when the required number of items have been selected.
This algorithm also ensures diversity by selecting one item from each cluster; the use of the Greedy Algorithm enables efficient diversity optimisation.
3. use of basic clustering methods: in Cluster-based Diversification, it is also common to use basic clustering methods. The following are typical methods.
- K-means clustering: the cluster centre is calculated and each data point is assigned to the nearest cluster.
- Hierarchical clustering: a method that combines clusters hierarchically, grouping similar ones together.
- DBSCAN: determines clusters by considering the density of data points. Areas of high density are grouped as clusters.
- Mean Shift: uses the density gradient of data points to find clusters. Clusters are formed by being attracted to the maximum local density.
These are common methods used in clustering and are chosen when applied to Cluster-based Diversification.
Application of Cluster-based Diversification.
Cluster-based Diversification has been used in various fields. The following are examples of its application.
1. product recommendation: Cluster-based Diversification is used for product recommendation in online stores and e-commerce sites, specifically by grouping similar products into the same cluster based on customers’ purchase history and preferences, and selecting products from different clusters to propose a sequence of diverse products. sequences are proposed.
For example, if a customer purchases a product from one cluster, not only can other products from the same cluster be recommended, but also products from different clusters can be recommended together to meet the customer’s new interests and needs.
2. tourist attraction route suggestions: in tourist apps and websites, clustering can be used to group similar tourist attractions and suggest tourist routes by selecting spots from different clusters.
This enables tourists to experience a variety of tourist experiences, not only spots of similar genres and characteristics, and suggests tourist routes selected from different categories, for example, historical spots, spots rich in nature, spots related to art and culture, etc.
3. building music playlists: in music streaming services, similar songs can be grouped into the same cluster based on the user’s listening history and preferences, and songs from different clusters can be selected to provide a diverse playlist.
This enables users to discover new music and a wider range of music experiences without getting bored with songs from the same genres and artists, for example, playlists with a selection of songs from different genres such as rock, pop, jazz and classical.
4. customised news feeds: in news apps and media sites, clustering can group similar news articles and provide customised news feeds by selecting articles from different clusters.
This allows users to browse articles from different perspectives and sources without being biased towards articles on similar topics or genres, and presents selected articles from different categories such as politics, economics, entertainment, science and sports.
5. suggested schedules for events and programmes: Cluster-based Diversification will also be used to suggest schedules for events and programmes such as conferences, seminars and festivals.
It is possible to group similar programmes and topics into the same cluster and select programmes from different clusters to provide a diversified schedule, e.g. different programmes such as specialised sessions, workshops, art and performances are proposed.
In these application cases, Cluster-based Diversification enables a diverse selection of items and events with different categories and characteristics, providing users with new experiences and information. There, it is important to select appropriate clustering methods and parameters according to data characteristics and task requirements.
Examples of Cluster-based Diversification implementations
The basic steps for implementing Cluster-based Diversification and an example implementation using Python are presented. The example describes building a simple product recommendation system with cluster-based diversification.
Procedure:
- Clustering of items: represent items as feature vectors and partition them into clusters based on similarity.
- Item selection per cluster: select one item from each cluster.
- Construction of recommendation sequences: present the selected items as a sequence.
Example implementation:
The following Python code is an example of building a simple product recommendation system with cluster-based diversity. K-means clustering is used for clustering.
First, import the necessary libraries.
import numpy as np
from sklearn.cluster import KMeansNext, the product data and feature vectors are prepared.
# Examples of product data
items = {
1: "Product A",
2: "Product B",
3: "Product C",
4: "Product D",
5: "Product E",
6: "Product F",
7: "Product G",
8: "Product H",
9: "Product I",
10: "ProductJ"
}
# Product feature vector (provisional example)
features = {
1: [0.1, 0.2],
2: [0.2, 0.3],
3: [0.8, 0.9],
4: [0.5, 0.6],
5: [0.3, 0.4],
6: [0.7, 0.8],
7: [0.6, 0.7],
8: [0.9, 0.8],
9: [0.4, 0.5],
10: [0.3, 0.6]
}Next, K-means clustering is performed to group items by cluster.
# Number of clusters
num_clusters = 3
# List of feature vectors
X = [features[i] for i in items.keys()]
# K-means clustering
kmeans = KMeans(n_clusters=num_clusters, random_state=0).fit(X)
# Clustering results
cluster_labels = kmeans.labels_
# Create a list of items per cluster.
cluster_items = defaultdict(list)
for i, cluster in enumerate(cluster_labels):
cluster_items[cluster].append(i+1)Finally, one item from each cluster is selected to build a recommendation sequence.
# Item selection per cluster.
recommended_sequence = []
for cluster, items in cluster_items.items():
recommended_sequence.append(items[0]) # Select the first item from each cluster.
# Display of recommended sequences.
print("Recommended Sequence:")
for item in recommended_sequence:
print(f"{item}: {items[item]}")In this example, the first item from each cluster is selected after clustering. The clustering method, the design of the feature vectors and the choice of the number of clusters should be adjusted appropriately according to the problem and the data.
The challenges of Cluster-based Diversification and how to deal with them.
Cluster-based Diversification is effective in building a recommendation system with diversity, but there are some challenges. The challenges and their countermeasures are described below.
1. selecting the number of clusters:
Challenge: choosing the right number of clusters is important, as an inappropriate number of clusters will degrade the performance of the recommendation system.
Solution: it is important to determine the appropriate number of clusters using cluster number selection methods such as the Elbow method and silhouette analysis, as well as adjusting the number of clusters based on domain knowledge and experiments.
2. clustering efficiency:
Challenge: clustering on large data sets is computationally expensive.
Solution: clustering efficiency can be improved using techniques such as data dimensionality reduction, sampling and parallel processing.
3. clustering quality:
Challenge: Insufficient clustering quality results in highly similar items being classified into different clusters and diversity is not ensured.
Solution: improve clustering quality by selecting appropriate distance measures and clustering methods, feature engineering and data pre-processing.
4. selection of appropriate features:
Challenge: insufficient feature richness of items results in inadequate clustering and poor quality recommendations.
Solution: data collection to obtain richer features, feature engineering, and possibly using embedding or deep learning to extract features.
5. balance between clusters:
Challenge: bias in the number of items between clusters leads to bias in the recommendation sequence.
Solution: after clustering, adopt a method to adjust the balance between clusters, e.g. by selecting the same number of items from each cluster.
6. diversity within clusters:
Challenge: insufficient diversity within clusters when items are similar even within the same cluster.
Solution: to maintain diversity within a cluster, the items to be selected should be chosen based on their similarity and distance from each other. This can be done by sub-clustering, for example.
7. overlapping clusters:
Challenge: overlapping recommendation sequences occur when items belong to more than one cluster.
Solution: use clustering methods and post-clustering post-processing to handle overlapping clusters appropriately.
Reference Information and Reference Books
For general machine learning algorithms including search algorithms, see “Algorithms and Data Structures” or “General Machine Learning and Data Analysis.
“Algorithms” and other reference books are also available.
Basic and Theoretical Background
1. Modern Information Retrieval
Authors: Ricardo Baeza-Yates, Berthier Ribeiro-Neto
Publisher: Addison-Wesley
Description: Covers information retrieval from basics to applications. There is also a chapter on diversification and its relation to clustering.
2. Introduction to Information Retrieval
Authors: Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze
Description: Basics of clustering and re-ranking in information retrieval. It is the basis for understanding cluster-based approaches.
Representative Papers
3. Maximal Marginal Relevance (MMR).
Carbonell, J., & Goldstein, J. (1998). The use of MMR, diversity-based reranking for reordering documents and producing summaries:
MMR is a pioneering idea in Cluster-based Diversification, a reranking method that balances similarity and novelty.
4.Explicit Search Result Diversification through Sub-queries
Application and Practice in Recommender Systems
6.Recommender Systems Handbook
Edited by Francesco Ricci, Lior Rokach, Bracha Shapira
Description: A comprehensive guide to recommender algorithms, including chapters such as “Beyond Accuracy: Evaluation of Recommender Systems”, which introduces diversification and cluster-based recommender strategies.
7.Fair Summarization: Bridging Quality and Diversity in Extractive Summaries
Useful resources for implementation and frameworks
8. Python Data Science Handbook – Jake VanderPlas
Description: Rich in clustering implementations using scikit-learn and other tools, this is a good starting point for developing search result diversification algorithms.
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