Personalized recommendation method based on concept lattice of linguistic value binary tuples

Abstract

The present invention relates to data mining and intelligent information processing technology, and in particular, to a personalized recommendation method based on a language-value binary concept lattice that can resolve recommendation interpretation ambiguity and cold start issues and can handle different data types. The method is performed in the following steps: data acquisition and preprocessing, collecting data information in multiple data formats, and determining whether the type of fuzzy data information collected by the computer is uniform. If not, the data type is converted into a language-value binary tuple: language-value binary concepts for user sets and item sets are constructed; language-value binary evaluation data for product decision attribute sets is obtained from users; a cognitive system for a training data set is constructed; a sufficient knowledge base and a fuzzy object language knowledge pseudo-lattice for the training data set are constructed; a necessary knowledge base and a fuzzy object language knowledge pseudo-lattice for the training data set are constructed; rules are extracted, selection and judgment are performed, and recommendation results are obtained.

Description

Technical Field

[0001] The present invention belongs to data mining and intelligent information processing technology, and in particular to a personalized recommendation method based on a language value binary concept lattice that can solve recommendation interpretation fuzziness and cold start problems and can process different data types. Background Art

[0002] As an important tool for data analysis and information processing, formal concept analysis has been widely used in fields such as machine learning, data mining, and information retrieval. Concept lattices offer significant advantages in rule extraction due to their ability to analyze relationships between objects and attributes and generate concepts within a formal context. Multiple rules can be derived through specific algorithms. Using linguistic values ​​to express the intrinsic properties between concepts can effectively reduce information loss caused by fuzzy and uncertain data, making concept lattices more suitable for uncertain knowledge processing and data mining. Introducing linguistic value models, particularly linguistic value binary models with data offsets, into concept lattices to obtain rules with linguistic value information has become a key area of ​​artificial intelligence, particularly in intelligent information processing, and a topic worthy of further study.

[0003] In real life, due to the complexity and uncertainty of human thinking, people's descriptions of things often exist in an imprecise and incomplete form. Professor Zadeh first proposed the fuzzy set theory in 1965, using this form to deal with uncertain information in real life. In response to complex qualitative problems that are difficult to evaluate with precise information, Zadeh used the concept of language variables to model language information, resulting in a fuzzy language method. The analysis, processing and application of language value information have aroused the research interest of scholars. Xu proposed a variety of generalized induced language aggregation operators and studied the relationship between each aggregation operator. Chen et al. proposed a proportional hesitant fuzzy language term set and studied its basic aggregation operator as a special method for language distribution evaluation. The proportional information of each generalized language term is used to express its possibility distribution under different assumptions.

[0004] Because fuzzy linguistic methods can cause a certain amount of information loss during the analysis and approximation process, the final result of linguistic information after fusion and discretization may deviate from the original cognition, leading to errors and lack of accuracy. Therefore, based on fuzzy linguistic calculation methods, Herrera et al. proposed a language value binary representation model, using symbolic offsets to supplement linguistic term information. This model allows for the continuous expression of linguistic value information across its domain, improving accuracy. Based on the hierarchical analysis method and fuzzy binary groups, Santos et al. proposed a decision support system that can integrate expert qualitative analysis and quantitative performance. Zhao et al. proposed a new TODIM method based on cumulative prospect theory and binary linguistic value sets, which fully considers the decision maker's psychology and ensures relatively objective attribute weights.

[0005] Currently, rule extraction has attracted the attention of many scholars and has been widely used in many fields, including machine learning, medical diagnosis, and financial investment. Because it can clearly reflect the relationship between objects and attributes, concept lattices have great advantages in rule extraction and have become an effective tool for association rule extraction. Godin et al. were the first to study the use of concept lattices to extract association rules and proposed a classic algorithm for extracting corresponding rules. Shao et al. defined discernibility matrices and discernibility functions and constructed a knowledge-free method to reduce complexity in the context of decision forms, ensuring that the maximum rules extracted from the simplified decision form context are the same as those extracted from the initial decision form context.

[0006] However, the existing application of concept lattice-based association rule extraction algorithms to recommendation systems still has problems such as fuzzy recommendation interpretation and cold start. In addition, since concept lattices still cannot handle different fuzzy data, it is easy to cause information processing difficulties. Summary of the Invention

[0007] The present invention belongs to data mining and intelligent information processing technology, and in particular to a personalized recommendation method based on a language value binary concept lattice that can solve recommendation interpretation fuzziness and cold start problems and can process different data types.

[0008] The technical solution of the present invention is: a personalized recommendation method based on a concept lattice of linguistic value two-tuples, which is carried out in the following steps:

[0009] A. Data collection and preprocessing:

[0010] A1. Collect data information in multiple data forms and determine whether the fuzzy data information types collected by the computer are uniform. If not uniform, the data types include fuzzy sets. Intuitionistic fuzzy sets and hesitant fuzzy sets Assume that the language term set is S={s0,s1...,s g}, α is a symbol offset, and the commodity attribute set is L = {l 1 ,l 2 ,…,l n}, the product set is U={u1,u2,…,u m}, n is the total number of product attributes, and m is the total number of products;

[0011] A2. Collect user feedback on product u r Use a data type to describe the item i The vague concept u r ∈U,l i ∈L, initialize the formal background of the product set U and the item attribute set L As a training set is the fuzzy binary relationship from the commodity set U to the commodity attribute set L, that is,

[0012] A3. The data type in is converted into a language value tuple according to the following conversion steps:

[0013] Judging data samples type;

[0014] If a[j] is a fuzzy set then

[0015] W:[0,1]→S×[-0.5,0.5)

[0016]

[0017] If a[j] is an intuitionistic fuzzy set, then

[0018] G:[0,1]×[0,1]→S×[-0.5,0.5)

[0019]

[0020] If a[j] is an intuitionistic fuzzy set, then

[0021] P:H(U)→S×[-0.5,0.5)

[0022]

[0023] Formal background based on product set U and product attribute set L All binary relations in the context are converted into language value binary tuples.

[0024] B. Define operations on the product set U and the product attribute set L: If X ⊙ =B⊙ At the same time B ⊙ =X ⊙ Then (X,B) is a two-tuple language concept.

[0025] Construct the concept of language value tuples of user set U and item set L;

[0026] C. Obtain the user's language value binary evaluation data for the product set U and the product decision attribute set D (if it is not a language value binary evaluation, convert it according to step A3) to form a language value binary form background

[0027] C1. Cognitive stage: based on training data set and form Construct training data set using X operator and B fuzzy operator cognitive systems;

[0028] C2. Based on training data set use definition Constructing a training dataset The sufficient knowledge base K1 and the language knowledge pseudo-form composed of K1;

[0029] C3. Based on training data set use definition Constructing a training dataset The necessary knowledge base K2 and the language knowledge pseudo-lattice composed of K2;

[0030] D. Personalized recommendation stage:

[0031] D1. Based on the training data set Using (X,B) is The concept of (Y, C) is The concept of X=Y, where Y≠U,φ, we get the rule B→C

[0032] D2. Analyze the extracted rules, make selection judgments, and obtain recommendation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a structural diagram of the fuzzy object language knowledge pseudo-lattice constructed in part K1 of the embodiment of the present invention.

[0034] Figure 2 This is a structural diagram of the fuzzy object language knowledge pseudo-lattice constructed in part K2 of the embodiment of the present invention.

[0035] Figure 3 This is a flow chart of the recommendation algorithm for the embodiment of the present invention. DETAILED DESCRIPTION

[0036] Taking the selection of Spring Festival commodities as an example, the personalized recommendation method based on the concept lattice of language value binary groups of the present invention is as follows: Figure 3 As shown, follow the steps below:

[0037] A. Data collection and preprocessing:

[0038] A1. Let the language term set be S = {s0,s1...,s g}, α is a symbol offset, and the commodity attribute set is L = {l 1 ,l 2 ,…,l n}, the product set is U={u1,u2,…,u m}, n is the total number of product attributes, and m is the total number of products;

[0039] Before the Chinese New Year, people traditionally buy decorations, hoping for good fortune and prosperity in the new year. To participate in Spring Festival promotions, users must purchase a product from a decoration store within a shopping system. Currently, six types of decorations are available at this store: small colored lanterns, lanterns, New Year paintings, ornaments, money trees, and Chinese knots. We have collected evaluations of these six decorations in terms of material, style, color, and durability, as well as user interest in and purchase intention for these new products. The collected user evaluations of these four features, material, style, color, and durability, are not all in the form of linguistic values; they also include other types of multi-type fuzzy data. Table 1 shows the complete fuzzy evaluation data for the decorations.

[0040] Table 1 Fuzzy evaluation information of decoration features

[0041]

[0042] Let the language term set S = {s0, s1..., s6} represent the different evaluation levels of users on the products. The granularity of this language term set is 7, where s0 to s6 represent "very bad", "very bad", "bad", "average", "good", "very good", and "very good" respectively.

[0043] The collected information about user consumption tendencies, that is, the evaluation information of users’ attention to new products and purchase intentions, is in the form of language value binary tuples. The specific information is shown in Table 2.

[0044] Table 2 Consumption tendency evaluation information

[0045]

[0046] A2. Collect user feedback on product u rUse a data type to describe the item i The vague concept u r ∈U,l i ∈L, initialize the formal background of the product set U and the item attribute set L As a training set is the fuzzy binary relationship from the commodity set U to the commodity attribute set L, that is,

[0047] A3. The data type in is converted into a language value tuple according to the following conversion steps:

[0048] Judging data samples type;

[0049] If a[j] is a fuzzy set then

[0050] W:[0,1]→S×[-0.5,0.5)

[0051]

[0052] If a[j] is an intuitionistic fuzzy set, then

[0053] G:[0,1]×[0,1]→S×[-0.5,0.5)

[0054]

[0055] If a[j] is an intuitionistic fuzzy set, then

[0056] P:H(U)→S×[-0.5,0.5)

[0057]

[0058] Formal background based on product set U and product attribute set L All binary relations in the context are converted into language value binary tuples. The results are shown in Table 3.

[0059] Table 3 Decoration feature evaluation language value binary form background

[0060]

[0061] B. Define operations on the product set U and the product attribute set L: If X ⊙ =B ⊙ At the same time B ⊙ =X ⊙Then (X, B) is a two-tuple language concept, and the language value two-tuple concept of user set U and item set L is constructed; 1#(Φ,{a(s5,-0.4),b(s5,0.4),c(s4,0.1),d(s4,-0.4)})

[0062] 2#({6},{a(s5,-0.4),b(s5,0.4),c(s6,0.1),d(s4,0.0)})

[0063] 3#({4,6},{a(s5,-0.4),c(s4,0.4),d(s4,-0.4)})

[0064] 4#({5,6},{a(s5,-0.4),b(s5,0.4),c(s5,0.2)})

[0065] 5#({1,3,6},{a(s5,-0.4),b(s5,0.4),d(s4,-0.4)})

[0066] 6#({1,5,6},{a(s5,-0.4),b(s5,0.4)})

[0067] 7#({4,5,6},{a(s5,-0.4),c(s4,0.4)})

[0068] 8#({1,3,4,6},{a(s5,-0.4),d(s4,0.0)})

[0069] 9#({1,3,5,6},{a(s5,-0.4),b(s5,0.4)})

[0070] 10#({2,4,5,6},{c(s4,0.1)})

[0071] 11#({1,3,4,5,6},{a(s5,-0.4)})

[0072] 12#({1,2,3,4,5,6},Φ)

[0073] C. Obtain the user's language value binary evaluation data for the product set U and the product decision attribute set D (if it is not a language value binary evaluation, convert it according to step A3) to form a language value binary form background

[0074] C1. Cognitive stage: based on training data set and form Construct training data set using X operator and B fuzzy operator The cognitive system of the obtained language value binary decision form background is shown in Table 4;

[0075] Table 4 Decoration evaluation information language value binary decision form background

[0076]

[0077] C2. Based on training data set use definition Constructing a training dataset The sufficient knowledge base K1 and the language knowledge pseudo-form composed of K1;

[0078] C3. Based on training data set use definition Constructing a training dataset The necessary knowledge base K2 and the language knowledge pseudo-lattice composed of K2;

[0079] Since there is too much fuzzy object language knowledge in the knowledge base K1 and the knowledge base K2, the embodiment of the present invention only provides part of the sufficient knowledge base K1 and part of the necessary knowledge base K2. Figure 1 and Figure 2 As shown;

[0080] D. Personalized recommendation stage:

[0081] D1. Based on the training data set Using (X,B) is The concept of (Y, C) is The concept satisfies X=Y, where Y≠U,φ, and we get the rule B→C.

[0082] D2. Analyze the extracted rules, make selection judgments, and obtain recommendation results.

[0083] From the rule {abcd}→{ef}, we can get the following information: If the user requires the Chinese knot to have a variety of styles, beautiful colors, good material and sturdiness, and the requirement for material is slightly higher than sturdiness, then he will be very willing to buy the Chinese knots of this store and pay more attention to the subsequent new products. In addition, due to e(s5,-0.2)

Claims

1. A personalized recommendation method based on the concept lattice of language value binary tuples, characterized by Follow these steps: A. Data collection and preprocessing: A1. Collect data in multiple data forms and determine whether the fuzzy data information types collected by the computer are uniform; if not uniform, determine whether the data types include fuzzy sets. , intuitionistic fuzzy sets and hesitant fuzzy sets ; Let the language term set be , is a symbol offset, and the product attribute set is , the product set is , n is the total number of product attributes, m is the total number of products; A2. Collect user feedback on products Use a data type to describe the item The vague concept , , initialize the product set U Set with item attributes Formal background As a training set For product set U To product attribute set The fuzzy binary relationship of ; A3. The data type in is converted into a language value tuple according to the following conversion steps: Judging data samples type; if For fuzzy sets ; if For intuitionistic fuzzy sets ; if For hesitant fuzzy sets ; Based on product set U Product attribute set Formal background All binary relations in the context are converted into language value binary pairs; B. Using product sets U Product attribute set The above operation is defined as: , , like at the same time but For the two-tuple language concept, construct the user set U With item set The concept of a linguistic valued tuple; C. Get the user's product set U Product decision attribute set If the non-language value binary evaluation data is converted according to step A3, a language value binary form background is formed. ; C1. Cognitive stage: based on training dataset and form .use Operator and Fuzzy operator construction training data set cognitive systems; C2. Based on training data set ,use definition . Constructing a training dataset A full knowledge base and The language knowledge constituted by the pseudo-case; C3. Based on training data set ,use definition . Constructing a training dataset A full knowledge base and The language knowledge constituted by the pseudo-case; D. Personalized recommendation stage: D1. Based on the training dataset ,use yes The concept, yes The concept of satisfaction , in , get the rules ; D2. Analyze the extracted rules, make selection judgments, and obtain recommendation results.

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